articles.bib

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@Article{AbdGad2012dynamic,
  author       = {Abdelkhalik, Ossama and Gad, Ahmed},
  title        = {Dynamic-Size Multiple Populations Genetic Algorithm for Multigravity-Assist Trajectory Optimization},
  journal      = jgcd,
  year         = 2012,
  volume       = 35,
  number       = 2,
  pages        = {520--529},
  doi          = {10.2514/1.54330}
}

@Article{AbrAmoDan1999,
  title =        {Simulated annealing cooling schedules for the school timetabling problem},
  author =       Abramson_D #and# {Amoorthy, Mohan Krishna and Dang, Henry},
  journal =      apjor,
  volume =       16,
  number =       1,
  pages =        {1--22},
  year =         1999,
}

@Article{Abramson1991,
  title =        {Constructing School Timetables Using Simulated Annealing: Sequential and Parallel Algorithms},
  author =       Abramson_D,
  journal =      ms,
  volume =       37,
  number =       1,
  pages =        {98--113},
  year =         1991,
  publisher =    informs
}

@Article{Ach2009mpc,
  author =       {Tobias Achterberg},
  title =        "{SCIP}: {Solving} constraint integer programs",
  journal =      mpc,
  year =         2009,
  volume =       1,
  number =       1,
  month =        jul,
  pages =        {1--41},
  epub =       {http://mpc.zib.de/archive/2009/1/Achterberg2009_Article_SCIPSolvingConstraintIntegerPr.pdf}
}

@Article{AchBer2007,
  title =        {Improving the feasibility pump},
  author =       {Achterberg, Tobias and Berthold, Timo},
  journal =      do,
  volume =       4,
  number =       1,
  pages =        {77--86},
  year =         2007,
  publisher =    elsevier
}

@Article{AcoMes2014jbi,
  author =       {H{\'e}ctor-Gabriel Acosta-Mesa and Fernando Rechy-Ram{\'i}rez}
                  #and# Mezura-Montes #and# {Nicandro Cruz-Ram{\'i}rez and
                  Hern{\'a}ndez Jim{\'e}nez, Rodolfo},
  title =       {Application of time series discretization using evolutionary programming for classification of precancerous cervical lesions},
  journal =     jbi,
  volume =      49,
  pages =       {73--83},
  year =        2014,
  doi =         {10.1016/j.jbi.2014.03.004},
  keywords =    {irace},
}

@Article{AddLocSch2008,
  title =        {Disk Packing in a Square: A New Global Optimization Approach},
  author =       {Addis, Bernardetta and Locatelli, Marco and Schoen, Fabio},
  journal =      informsjoc,
  year =         2008,
  number =       4,
  pages =        {516--524},
  volume =       20,
  doi =          {10.1287/ijoc.1080.0263},
  alias =        "Addis2008"
}

@Article{Ade92,
  author =       {B. Adenso-D{\'i}az},
  title =        {Restricted Neighborhood in the Tabu Search for the
                  Flowshop Problem},
  journal =      ejor,
  year =         1992,
  volume =       62,
  number =       1,
  pages =        {27--37}
}

@Article{AdeLag06tuning,
  author =       {B. Adenso-D{\'i}az} #and# Laguna,
  title =        {Fine-Tuning of Algorithms Using Fractional
                  Experimental Design and Local Search},
  journal =      or,
  year =         2006,
  volume =       54,
  number =       1,
  pages =        {99--114},
  keywords =      "Calibra",
}

@Article{AdrBieSha2022jair,
  title =        {Automated dynamic algorithm configuration},
  author =       Adriaensen_S #and# Biedenkapp #and# {Shala, Gresa and Awad, Noor and Eimer, Theresa} #and# Lindauer_M #and# Hutter,
  journal =      jair,
  volume =       75,
  pages =        {1633--1699},
  year =         2022,
  doi =          {10.1613/jair.1.13922}
}

@Article{AfsMieRui2021survey,
  author =       {Afsar, Bekir} #and# Miettinen #and# Ruiz_Francisco,
  title =        {Assessing the Performance of Interactive Multiobjective
                  Optimization Methods: A Survey},
  year =         2021,
  volume =       54,
  number =       4,
  doi =          {10.1145/3448301},
  abstract =     {Interactive methods are useful decision-making tools for
                  multiobjective optimization problems, because they allow a
                  decision-maker to provide her/his preference information
                  iteratively in a comfortable way at the same time as (s)he
                  learns about all different aspects of the problem. A wide
                  variety of interactive methods is nowadays available, and
                  they differ from each other in both technical aspects and
                  type of preference information employed. Therefore, assessing
                  the performance of interactive methods can help users to
                  choose the most appropriate one for a given problem. This is
                  a challenging task, which has been tackled from different
                  perspectives in the published literature. We present a
                  bibliographic survey of papers where interactive
                  multiobjective optimization methods have been assessed
                  (either individually or compared to other methods). Besides
                  other features, we collect information about the type of
                  decision-maker involved (utility or value functions,
                  artificial or human decision-maker), the type of preference
                  information provided, and aspects of interactive methods that
                  were somehow measured. Based on the survey and on our own
                  experiences, we identify a series of desirable properties of
                  interactive methods that we believe should be assessed.},
  journal =      acm-cs,
  numpages =     27,
  keywords =     {decision-makers, Interactive methods, performance assessment,
                  preference information, multiobjective optimization problems}
}

@Article{AfsSilMis2022design,
  author       = {Afsar, Bekir} #and# {Silvennoinen, Johanna} #and# {Misitano,
                  Giovanni} #and# Ruiz_Francisco #and# {Ruiz, Ana B.} #and#
                  Miettinen,
  title        = {Designing empirical experiments to compare interactive
                  multiobjective optimization methods},
  journal      = jors,
  year         = 2022,
  volume       = 74,
  number       = 11,
  pages        = {2327--2338},
  month        = nov,
  doi          = {10.1080/01605682.2022.2141145}
}

@Article{AgoPea1973normality,
  doi =          {10.2307/2335012},
  year =         1973,
  month =        dec,
  publisher =    {{JSTOR}},
  volume =       60,
  number =       3,
  pages =        {613--622},
  author =       {Ralph {D'Agostino} and E. S. Pearson},
  title =        {Tests for Departure from Normality. Empirical Results for the
                  Distributions of $b_2$ and $\surd b_1$},
  journal =      biometrika
}

@Article{Agrell1997ejor,
  title =        {On redundancy in multi criteria decision making},
  journal =      ejor,
  volume =       98,
  number =       3,
  pages =        {571--586},
  year =         1997,
  doi =          {10.1016/0377-2217(95)00340-1},
  author =       {Per J. Agrell},
  keywords =     {Multi criteria decision making, Redundancy, objective
                  reduction, Vector optimisation},
  abstract =     {The concept of redundancy is accepted in Operations Research
                  and Information Theory. In Linear Programming, a constraint
                  is said to be redundant if the feasible decision space is
                  identical with or without the constraint. In Information
                  Theory, redundancy is used as a measure of the stability
                  against noise in transmission. Analogies with Multi Criteria
                  Decision Making (MCDM) are indicated and it is argued that
                  the redundancy concept should be used as a regular feature in
                  conditioning and analysis of Multi Criteria
                  Programs. Properties of a proposed conflict-based
                  characterisation are stated and some existence results are
                  derived. Redundancy is here intended for interactive methods,
                  when the efficient set is progressively explored. A new
                  redundancy test for the linear case is formulated from the
                  framework. A probabilistic method based on correlation is
                  proposed and tested for the non-linear case. Finally, some
                  general guidelines are given concerning the redundancy
                  problem.}
}

@Article{AguTan2007ejor,
  title =        "Working principles, behavior, and performance of {MOEAs} on
                  {MNK}-landscapes",
  author =       Aguirre #and# Tanaka,
  journal =      ejor,
  volume =       181,
  number =       3,
  year =         2007,
  pages =        {1670--1690},
  doi =          {10.1016/j.ejor.2006.08.004}
}

@Article{AhmOsm2004:aor,
  author =       {Samad Ahmadi} #and# Osman,
  title =        {Density Based Problem Space Search for the Capacitated Clustering $p$-Median Problem},
  journal =      aor,
  year =         2004,
  volume =       131,
  pages =        {21--43}
}

@Article{AhrElsSarEss2021weighted,
  title =        {Weighted pointwise prediction method for dynamic
                  multiobjective optimization},
  journal =      is,
  volume =       546,
  pages =        {349--367},
  year =         2021,
  author =       {Ali Ahrari and Saber Elsayed and Ruhul Sarker and Daryl
                  Essam} #and# Coello,
}

@Article{AhuErgOrlPun2002:dam,
  author =       Ahuja_RK #and# {O. Ergun} #and# {A. P. Punnen},
  title =        {A Survey of Very Large-scale Neighborhood Search
                  Techniques},
  journal =      dam,
  year =         2002,
  volume =       123,
  number =       {1--3},
  pages =        {75--102}
}

@Article{AinKumCha2009asc,
  author =       Aine_S #and# Kumar_Rajeev #and# Chakrabarti_PP,
  title =        {Adaptive parameter control of evolutionary
                  algorithms to improve quality-time trade-off},
  journal =      asoco,
  volume =       9,
  number =       2,
  year =         2009,
  pages =        {527--540},
  doi =          {10.1016/j.asoc.2008.07.001},
  keywords =     {anytime},
}

@Article{AlbLanSte2010,
  author =       {Albrecht, A. A. and Lane, P. C. R. and Steinh{\"o}fel, K.},
  title =        {Analysis of Local Search Landscapes for k-{SAT} Instances},
  journal =      mcs,
  number =       4,
  pages =        {465--488},
  volume =       3,
  year =         2010,
  doi =          {10.1007/s11786-010-0040-7}
}

@Article{Albers2003online,
  title =        {Online Algorithms: A Survey},
  author =       {Albers, Susanne},
  journal =      mp,
  year =         2003,
  number =       1,
  pages =        {3--26},
  volume =       97,
}

@Article{AleMos2016slr,
  author =       {Aldeida Aleti and Irene Moser},
  year =         2016,
  title =        {A systematic literature review of adaptive parameter control
                  methods for evolutionary algorithms},
  journal =      acm-cs,
  volume =       49,
  number =       "3, Article 56",
  month =        oct,
  pages =        35,
  doi =          {10.1145/2996355}
}

@Article{AlfRuiPagStu2020hybrid,
  title =        {Automatic Algorithm Design for Hybrid Flowshop Scheduling
                  Problems},
  author =       Alfaro-Fernandez #and# Ruiz_Ruben #and# Pagnozzi #and#
                  Stuetzle,
  journal =      ejor,
  volume =       282,
  number =       3,
  pages =        {835--845},
  year =         2020,
  doi =          {10.1016/j.ejor.2019.10.004},
  keywords =     {Scheduling, Hybrid flowshop, Automatic algorithm
                  configuration, Automatic Algorithm Design},
  abstract =     {Industrial production scheduling problems are challenges that
                  researchers have been trying to solve for decades. Many
                  practical scheduling problems such as the hybrid flowshop are
                  NP-hard. As a result, researchers resort to metaheuristics to
                  obtain effective and efficient solutions. The traditional
                  design process of metaheuristics is mainly manual, often
                  metaphor-based, biased by previous experience and prone to
                  producing overly tailored methods that only work well on the
                  tested problems and objectives. In this paper, we use an
                  Automatic Algorithm Design (AAD) methodology to eliminate
                  these limitations. AAD is capable of composing algorithms
                  from components with minimal human intervention. We test the
                  proposed AAD for three different optimization objectives in
                  the hybrid flowshop. Comprehensive computational and
                  statistical testing demonstrates that automatically designed
                  algorithms outperform specifically tailored state-of-the-art
                  methods for the tested objectives in most cases.}
}

@Article{AliMei2011kemeny,
  annote =       {Computational Foundations of Social Choice},
  year =         2012,
  month =        jul,
  publisher =    {Elsevier {BV}},
  volume =       64,
  number =       1,
  pages =        {28--40},
  author =       {Alnur Ali and Marina Meil{\u{a}}},
  doi =          {10.1016/j.mathsocsci.2011.08.008},
  journal =      mss,
  title =        {Experiments with {Kemeny} ranking: What Works When?},
  keywords =     "Borda ranking, Kemeny ranking"
}

@Article{AllAyd2013,
  title =        {Algorithms for no-wait flowshops with total
                  completion time subject to makespan},
  author =       {Allahverdi, Ali and Aydilek, Harun},
  journal =      ijamt,
  pages =        {1--15},
  year =         2013
}

@Article{AllJasLieTam2022cor,
  title =        {What if we increase the number of objectives? {Theoretical}
                  and empirical implications for many-objective combinatorial
                  optimization},
  author =       Allmendinger #and# Jaszkiewicz #and# Liefooghe #and# {Tammer,
                  Christiane},
  doi =          {10.1016/j.cor.2022.105857},
  journal =      cor,
  volume =       145,
  pages =        105857,
  year =         2022,
  publisher =    {Elsevier}
}

@Article{AllKno2013ephemeral,
  title =        {On Handling Ephemeral Resource Constraints in Evolutionary
                  Search},
  author =       Allmendinger #and# Knowles,
  year =         2013,
  month =        sep,
  journal =      ec,
  volume =       21,
  number =       3,
  pages =        {497--531},
  issn =         {1063-6560, 1530-9304},
  doi =          {10.1162/EVCO_a_00097},
  abstract =     {We consider optimization problems where the set of solutions
                  available for evaluation at any given time t during
                  optimization is some subset of the feasible space. This model
                  is appropriate to describe many closed-loop optimization
                  settings (i.e. where physical processes or experiments are
                  used to evaluate solutions) where, due to resource
                  limitations, it may be impossible to evaluate particular
                  solutions at particular times (despite the solutions being
                  part of the feasible space). We call the constraints
                  determining which solutions are non-evaluable ephemeral
                  resource constraints (ERCs). In this paper, we investigate
                  two specific types of ERC: one encodes periodic resource
                  availabilities, the other models `commitment' constraints
                  that make the evaluable part of the space a function of
                  earlier evaluations conducted. In an experimental study, both
                  types of constraint are seen to impact the performance of an
                  evolutionary algorithm significantly. To deal with the
                  effects of the ERCs, we propose and test five different
                  constrainthandling policies (adapted from those used to
                  handle `standard' constraints), using a number of different
                  test functions including a fitness landscape from a real
                  closed-loop problem. We show that knowing information about
                  the type of resource constraint in advance may be sufficient
                  to select an effective policy for dealing with it, even when
                  advance knowledge of the fitness landscape is limited.},
  langid =       {english}
}

@Article{Alm10,
  author =       Almeder,
  title =        {A hybrid optimization approach for multi-level
                  capacitated lot-sizing problems},
  number =       2,
  journal =      ejor,
  year =         2010,
  keywords =     {Ant colony optimization, Manufacturing, Material
                  requirements planning, Mixed-integer programming},
  pages =        {599--606},
  volume =       200,
  doi =          {10.1016/j.ejor.2009.01.019},
  abstract =     {Solving multi-level capacitated lot-sizing problems
                  is still a challenging task, in spite of increasing
                  computational power and faster algorithms. In this
                  paper a new approach combining an ant-based
                  algorithm with an exact solver for (mixed-integer)
                  linear programs is presented. A {MAX-MIN} ant system
                  is developed to determine the principal production
                  decisions, a {LP/MIP} solver is used to calculate
                  the corresponding production quantities and
                  inventory levels. Two different local search methods
                  and an improvement strategy based on reduced
                  mixed-integer problems are developed and integrated
                  into the ant algorithm. This hybrid approach
                  provides superior results for small and medium-sized
                  problems in comparison to the existing approaches in
                  the literature. For large-scale problems the
                  performance of this method is among the best},
}

@Article{AluKat2004:ieee,
  author =       {S. Alupoaei and S. Katkoori},
  title =        {Ant Colony System Application to Marcocell Overlap Removal},
  journal =      tvlsis,
  year =         2004,
  volume =       12,
  number =       10,
  pages =        {1118--1122}
}

@Article{Amaral2012corridor,
  title =        "The Corridor Allocation Problem",
  journal =      cor,
  volume =       39,
  number =       12,
  pages =        "3325--3330",
  year =         2012,
  author =       "Amaral, Andr{\'e} R. S.",
  doi =          "10.1016/j.cor.2012.04.016",
  keywords =     "Facility layout, Double row layout, Integer programming",
  abstract =     "The corridor allocation problem (CAP) seeks an arrangement of
                  facilities along a central corridor defined by two horizontal
                  lines parallel to the x-axis of a Cartesian coordinate
                  system. The objective is to minimize the total communication
                  cost among facilities, while respecting two main conditions:
                  (i) no space is allowed between two adjacent facilities; (ii)
                  the left-most point of the arrangement on either line should
                  have zero abscissa. The conditions (i) and (ii) are required
                  in many applications such as the arrangement of rooms at
                  office buildings or hospitals. The CAP is a NP-Hard
                  problem. In this paper, a mixed-integer programming
                  formulation of the CAP is proposed, which allows us to
                  compute optimal layouts in reasonable time for problem
                  instances of moderate sizes. Moreover, heuristic procedures
                  are presented that can handle larger instances."
}

@Article{AmiBadFar2007:cis,
  author =       {Amir, C. and Badr, A. and Farag, I},
  title =        {A Fuzzy Logic Controller for Ant Algorithms},
  journal =      cis,
  year =         2007,
  volume =       11,
  number =       2,
  pages =        {26--34}
}

@Article{AndDefDouJor2003,
  title =        {An Introduction to {MCMC} for Machine Learning},
  author =       Andrieu_C #and# DeFreitas_N #and# Doucet_A #and# Jordan_MI,
  journal =      ml,
  volume =       50,
  number =       {1-2},
  pages =        {5--43},
  year =         2003,
  publisher =    springer
}

@Article{AndFagHob2015maritime,
  author       = {Andersson, Henrik and Fagerholt, Kjetil and Hobbesland,
                  Kirsti},
  title        = {Integrated maritime fleet deployment and speed optimization:
                  Case study from {RoRo} shipping},
  journal      = cor,
  year         = 2015,
  volume       = 55,
  pages        = {233--240},
  month        = mar,
  doi          = {10.1016/j.cor.2014.03.017}
}

@Article{AndJorLin1996,
  author =       {Andersen, K. A. and J{\"o}rnsten, K. and Lind, M.},
  title =        {On bicriterion minimal spanning trees: An approximation},
  journal =      cor,
  volume =       23,
  number =       12,
  pages =        {1171--1182},
  year =         1996,
}

@Article{AnejaNair79,
  author =       {Aneja, Y. P. and Nair, K. P. K.},
  title =        {Bicriteria Transportation Problem},
  journal =      ms,
  volume =       25,
  number =       1,
  pages =        {73--78},
  year =         1979,
}

@Article{AngBamGou2004tcs,
  author =       {Eric Angel} #and# {Evripidis Bampis} #and# {Laurent
                  Gourv{\'e}s},
  title =        {Approximating the {Pareto} curve with local search
                  for the bicriteria {TSP}(1,2) problem},
  journal =      tcs,
  number =       {1-3},
  pages =        {135--146},
  volume =       310,
  year =         2004,
  doi =          {10.1016/S0304-3975(03)00376-1},
  keywords =     {Archiving, Local search, Multicriteria TSP,
                  Approximation algorithms}
}

@Article{AngWoo09,
  author =       Angus #and# "Clinton Woodward",
  title =        {Multiple Objective Ant Colony Optimisation},
  journal =      swarm,
  year =         2009,
  volume =       3,
  number =       1,
  pages =        {69--85},
  doi =          {10.1007/s11721-008-0022-4},
}

@Article{AnjVie2017flp,
  title =        {Mathematical optimization approaches for facility layout
                  problems: The state-of-the-art and future research
                  directions},
  author =       {Anjos, Miguel F. and Vieira, Manuel V. C.},
  journal =      ejor,
  volume =       261,
  number =       1,
  pages =        {1--16},
  year =         2017,
  publisher =    {Elsevier}
}

@Article{AnsBriGou2002qap,
  author =       {Kurt Anstreicher and Nathan Brixius and Jean-Pierre Goux and
                  Jeff Linderoth},
  title =        {Solving large quadratic assignment problems on computational
                  grids},
  doi =          {10.1007/s101070100255},
  year =         2002,
  month =        feb,
  volume =       91,
  number =       3,
  pages =        {563--588},
  journal =      mpb,
  abstract =     "The quadratic assignment problem (QAP) is among the hardest
                  combinatorial optimization problems. Some instances of size
                  $n = 30$ have remained unsolved for decades. The solution of
                  these problems requires both improvements in mathematical
                  programming algorithms and the utilization of powerful
                  computational platforms. In this article we describe a novel
                  approach to solve QAPs using a state-of-the-art
                  branch-and-bound algorithm running on a federation of
                  geographically distributed resources known as a computational
                  grid. Solution of QAPs of unprecedented complexity, including
                  the nug30, kra30b, and tho30 instances, is reported."
}

@Article{AppBixChvCoo03:mp,
  author =       Applegate_D #and# Bixby #and# Chvatal #and# Cook_W,
  title =        {Implementing the {Dantzig}-{Fulkerson}-{Johnson} Algorithm for Large Traveling Salesman Problems},
  journal =      mpb,
  year =         2003,
  volume =       97,
  number =       {1--2},
  pages =        {91--153},
  doi = "10.1007/s10107-003-0440-4"
}

@Article{AppBixChvCoo98,
  author  =      Applegate_D #and# Bixby #and# Chvatal #and# Cook_W,
  title   =      "On the Solution of Traveling Salesman Problems",
  journal =      "Documenta Mathematica",
  year    =      1998,
  volume  =      "Extra Volume ICM III",
  pages   =      "645--656",
}

@Article{AppBlaNew1961,
  author       = {Appleby, J. S. and Blake, D. V. and Newman, E. A.},
  title        = {Techniques for producing school timetables on a computer and
                  their application to other scheduling problems},
  journal      = comjnl,
  year         = 1961,
  volume       = 3,
  number       = 4,
  pages        = {237--245},
  doi          = {10.1093/comjnl/3.4.237}
}

@Article{AppCoo91,
  author =       Applegate_D #and# Cook_W,
  title =        "A Computational Study of the Job-Shop Scheduling
                  Problem",
  journal =      orsa,
  year =         1991,
  volume =       3,
  number =       2,
  pages =        "149--156",
}

@Article{AppCooRoh2003,
  title =        {Chained {Lin}-{Kernighan} for Large Traveling Salesman
                  Problems},
  author =       Applegate_D #and# Cook_W #and# " Andr{\'e} Rohe",
  journal =      informsjoc,
  volume =       15,
  number =       1,
  pages =        {82--92},
  year =         2003,
  alias =        "AppCooRoh99",
  doi =          "10.1287/ijoc.15.1.82.15157"
}

@Article{AppEtAl09,
  author =       Applegate_D #and# Bixby #and# Chvatal #and# Cook_W #and# "
                  D. Espinoza and M. Goycoolea " #and# Helsgaun,
  title =        {Certification of an Optimal {TSP} Tour Through 85,900 Cities},
  journal =      orl,
  volume =       37,
  number =       1,
  year =         2009,
  pages =        {11--15}
}

@Article{AraCamCam2022openletter,
  author =       Aranha #and# Camacho_C #and# Campelo #and# Dorigo #and# Ruiz_Ruben #and# Sevaux_M #and# Sorensen  #and# Stuetzle,
  title =        {Metaphor-based Metaheuristics, a Call for Action: the Elephant in the Room},
  journal =      swarm,
  pages =        {1--6},
  volume =       16,
  number =       1,
  doi =          "10.1007/s11721-021-00202-9",
  year =         2022
}

@Article{ArcSavSpe2016vehicle,
  title =        {The Vehicle Routing Problem with Occasional Drivers},
  author =       {Archetti, Claudia} #and# Savelsbergh_M #and# Grazia_Speranza,
  journal =      ejor,
  volume =       254,
  number =       2,
  pages =        {472--480},
  year =         2016,
  doi =          "10.1016/j.ejor.2016.03.049",
  publisher =    {Elsevier}
}

@Article{ArnSanSorVid2019,
  author       = Arnold_F #and# {Santana, \'{I}talo} #and# Sorensen #and#
                  Vidal_T,
  title        = {{PILS}: Exploring high-order neighborhoods by pattern mining
                  and injection},
  journal      = arxiv # {1912.11462 [cs.AI]},
  year         = 2019,
  doi          = {10.48550/arXiv.1912.11462}
}

@Article{ArnSor2019knowledge,
  author       = Arnold_F #and# Sorensen,
  title        = {Knowledge-guided local search for the vehicle routing
                  problem},
  journal      = cor,
  year         = 2019,
  volume       = 105,
  pages        = {32--46},
  doi          = {10.1016/j.cor.2019.01.002}
}

@Article{ArnSor2019vrp,
  author       = Arnold_F #and# Sorensen,
  title        = {What makes a {VRP} solution good? The generation of
                  problem-specific knowledge for heuristics},
  journal      = cor,
  year         = 2019,
  volume       = 106,
  pages        = {280--288},
  doi          = {10.1016/j.cor.2018.02.007}
}

@Article{AroKadKhu2006,
  title =        {An empirical comparison of tabu search, simulated annealing,
                  and genetic algorithms for facilities location problems},
  author =       {Arostegui Jr, Marvin A. and Kadipasaoglu, Sukran N. and Khumawala, Basheer M.},
  journal =      ijpe,
  volume =       103,
  number =       2,
  pages =        {742--754},
  year =         2006,
  publisher =    elsevier
}

@Article{Arr04,
  title =        "A partial enumeration heuristic for multi-objective
                  flowshop scheduling problems",
  author =       Arroyo_JEC #and# Armentano,
  journal =      jors,
  volume =       55,
  number =       9,
  pages =        {1000--1007},
  year =         2004,
}

@Article{ArrArm05,
  author =       Arroyo_JEC #and# Armentano,
  title =        "Genetic local search for multi-objective flowshop
                  scheduling problems",
  journal =      ejor,
  volume =       167,
  number =       3,
  pages =        "717--738",
  year =         2005,
  keywords =       "Multicriteria Scheduling",
}

@Article{ArrLeu2017,
  author = 	 Arroyo_JEC #and# Leung_JYT,
  title = 	 {An Effective Iterated Greedy Algorithm for Scheduling Unrelated Parallel Batch Machines with Non-identical Capacities and Unequal Ready Times},
  journal = 	 cie,
  year = 	 2017,
  volume = 	 105,
  pages = 	 {84--100}
}

@Article{ArzCebIru2022jcgs,
  author       = {Arza, Etor} #and# Ceberio #and# Irurozki_E #and# {P{\'e}rez,
                  Aritz},
  title        = {Comparing Two Samples Through Stochastic Dominance: A
                  Graphical Approach},
  journal      = jcgs,
  year         = 2022,
  pages        = {1--38},
  month        = jun,
  doi          = {10.1080/10618600.2022.2084405}
}

@Article{Asch01tsptw,
  author =       Ascheuer #and# Fischetti #and# Groetschel,
  title =        {Solving asymmetric travelling salesman problem with
                  time windows by branch-and-cut},
  journal =      mp,
  year =         2001,
  volume =       90,
  pages =        {475--506}
}

@Article{AssWanFre2014hetero,
  author =       {John{-}Alexander M. Assael and Ziyu Wang} #and# DeFreitas_N,
  title =        {Heteroscedastic Treed Bayesian Optimisation},
  journal =      arxiv # {1410.7172},
  doi =          "10.48550/arXiv.1410.7172",
  year =         2014,
  eprinttype =   {arXiv},
  eprint =       {1410.7172},
  keywords =     "Treed-GP"
}

@Article{Ata2003mik,
  author   = Atamturk_A,
  title    = {On the facets of the mixed--integer knapsack polyhedron},
  journal  = mp,
  year     = 2003,
  volume   = 98,
  number   = 1,
  pages    = {145--175},
  doi      = {10.1007/s10107-003-0400-z}
}

@Article{AudDanOrb2014,
  author = 	 Audet_C #and# Dang_CK #and# Orban_D,
  title = 	 {Optimization of Algorithms with {OPAL}},
  journal = 	 mpc,
  year = 	 2014,
  volume = 	 6,
  number = 	 3,
  pages = 	 {233--254}
}

@Article{AudEgla1977,
  title =        {New approach to the design of multifactor experiments},
  author =       {Audze, P. and Egl{\~a}js, Vilnis},
  journal =      {Problems of Dynamics and Strengths},
  year =         1977,
  note =         {(in Russian)},
  pages =        {104--107},
  volume =       35,
  publisher =    {Zinatne Publishing House, Riga},
  alias =        "Audze1977"
}

@Article{AudOrb06:mads,
  author =       Audet_C #and# Orban_D,
  title =        {Finding Optimal Algorithmic Parameters Using Derivative-Free
                  Optimization},
  journal =      siamo,
  year =         2006,
  volume =       17,
  number =       3,
  pages =        {642--664},
  keywords =     "mesh adaptive direct search; pattern search",
  doi =          "10.1137/0406208"
}

@Article{Aue2002using,
  title =        {Using Confidence Bounds for Exploitation-Exploration
                  Trade-offs},
  author =       {Auer, Peter},
  journal =      jmlr,
  volume =       3,
  month =        nov,
  pages =        {397--422},
  year =         2002,
  abstract =     "We show how a standard tool from statistics --- namely
                  confidence bounds --- can be used to elegantly deal with
                  situations which exhibit an exploitation-exploration
                  trade-off. Our technique for designing and analyzing
                  algorithms for such situations is general and can be applied
                  when an algorithm has to make exploitation-versus-exploration
                  decisions based on uncertain information provided by a random
                  process.  We apply our technique to two models with such an
                  exploitation-exploration trade-off. For the adversarial
                  bandit problem with shifting our new algorithm suffers only
                  $O((ST)^{1/2})$ regret with high probability over $T$ trials
                  with $S$ shifts. Such a regret bound was previously known
                  only in expectation. The second model we consider is
                  associative reinforcement learning with linear value
                  functions. For this model our technique improves the regret
                  from $O(T^{3/4})$ to $O(T^{1/2})$."
}

@Article{AueCesFis2002finite,
  title =        {Finite-time analysis of the multiarmed bandit problem},
  author =       {Auer, Peter and Cesa-Bianchi, Nicolo and Fischer, Paul},
  journal =      ml,
  volume =       47,
  number =       {2-3},
  pages =        {235--256},
  year =         2002,
}

@Article{AugBadBroZit2012tcs,
  author =       Auger_A #and# Bader_J #and# Brockhoff #and# Zitzler,
  title =        {Hypervolume-based multiobjective optimization:
                  Theoretical foundations and practical implications},
  journal =      tcs,
  volume =       425,
  year =         2012,
  pages =        {75--103},
  doi =          "10.1016/j.tcs.2011.03.012",
}

@Article{AvcTop2017:cor,
  author = 	 {Mustafa Avci and Seyda Topaloglu},
  title = 	 {A Multi-start Iterated Local Search Algorithm for the Generalized Quadratic Multiple Knapsack Problem},
  journal = 	 cor,
  year = 	 2017,
  volume = 	 83,
  pages = 	 {54--65}
}

@Article{AvrAllLop2021arxiv,
  author =       Avramescu_A #and# Allmendinger #and# Lopez-Ibanez,
  title =        "Managing Manufacturing and Delivery of Personalised Medicine:
                  Current and Future Models",
  year =         2021,
  journal =      arxiv # {2105.12699 [econ.GN]},
  url =          {https://arxiv.org/abs/2105.12699}
}

@Article{AydYavStu2017:si,
  author = 	 Dogan #and# Yavuz #and# Stuetzle,
  title = 	 {{ABC-X:} A Generalized, Automatically Configurable Artificial Bee
               Colony Framework},
  journal = 	 swarm,
  year = 	 2017,
  volume = 	 11,
  number = 	 1,
  pages = 	 {1--38}
}

@Article{AyoAllLopPar2022scalarisation,
  author       = Ayodele_M #and# Allmendinger #and# Lopez-Ibanez #and# Parizy,
  title        = {A Study of Scalarisation Techniques for Multi-Objective
                  {QUBO} Solving},
  journal      = arxiv # {2210.11321},
  year         = 2022,
  doi          = {10.48550/arXiv.2210.11321}
}

@Article{AziTay2014eaai,
  author =       {Mahdi Aziz and {Tayarani-N}, Mohammad-H.},
  title =        {An adaptive memetic Particle Swarm Optimization algorithm for finding large-scale Latin hypercube designs},
  journal =      eaai,
  volume =       36,
  pages =        {222--237},
  year =         2014,
  doi =          {10.1016/j.engappai.2014.07.021},
  keywords =     {F-race},
}

@Article{BacHelPic2020gaussian,
  title =        {Gaussian process optimization with failures: Classification
                  and convergence proof},
  author =       {Bachoc, Fran{\c c}ois and Helbert, C{\'e}line and Picheny,
                  Victor},
  journal =      jgo,
  year =         2020,
  epub =
                  {https://hal.archives-ouvertes.fr/hal-02100819/file/optimwithfailurerevised_hal.pdf},
  keywords =     "crashed simulation; latent gaussian process; automotive fan
                  design; industrial application; GP classification; Expected
                  Feasible Improvement with Gaussian Process Classification
                  with signs; EFI GPC sign",
  doi =          {10.1007/s10898-020-00920-0},
  abstract =     "We consider the optimization of a computer model where each
                  simulation either fails or returns a valid output
                  performance. We first propose a new joint Gaussian process
                  model for classification of the inputs (computation failure
                  or success) and for regression of the performance
                  function. We provide results that allow for a computationally
                  efficient maximum likelihood estimation of the covariance
                  parameters, with a stochastic approximation of the likelihood
                  gradient. We then extend the classical improvement criterion
                  to our setting of joint classification and regression. We
                  provide an efficient computation procedure for the extended
                  criterion and its gradient. We prove the almost sure
                  convergence of the global optimization algorithm following
                  from this extended criterion. We also study the practical
                  performances of this algorithm, both on simulated data and on
                  a real computer model in the context of automotive fan
                  design."
}

@Article{BadZit2011ec,
  author =       Bader_J #and# Zitzler,
  title =        "{HypE}: An Algorithm for Fast Hypervolume-Based
                  Many-Objective Optimization",
  journal =      ec,
  volume =       19,
  number =       1,
  year =         2011,
  pages =        {45--76},
  doi =          {10.1162/EVCO_a_00009},
}

@Article{BahComLau2019tre,
  title =        {Bi-objective multi-layer location--\hspace{0pt}allocation model for the
                  immediate aftermath of sudden-onset disasters},
  author =       {Baharmand, Hossein and Comes, Tina and Lauras, Matthieu},
  journal =      tre,
  volume =       127,
  pages =        {86--110},
  year =         2019,
  doi =          {10.1016/j.tre.2019.05.002},
  abstract =     "Locating distribution centers is critical for humanitarians
                  in the immediate aftermath of a sudden-onset disaster. A
                  major challenge lies in balancing the complexity and
                  uncertainty of the problem with time and resource
                  constraints. To address this problem, we propose a
                  location-allocation model that divides the topography of
                  affected areas into multiple layers; considers constrained
                  number and capacity of facilities and fleets; and allows
                  decision-makers to explore trade-offs between response time
                  and logistics costs. To illustrate our theoretical work, we
                  apply the model to a real dataset from the 2015 Nepal
                  earthquake response. For this case, our method results in a
                  considerable reduction of logistics costs."
}

@Article{Baker2016reprod,
  title =        {Is there a reproducibility crisis?},
  author =       {Monya Baker},
  journal =      nature,
  volume =       533,
  pages =        {452--454},
  year =         2016,
}

@Article{Baker83:tsptw,
  author =       {Edward K. Baker},
  title =        "An Exact Algorithm for the Time-Constrained
                  Traveling Salesman Problem",
  volume =       31,
  doi =         {10.1287/opre.31.5.938},
  number =       5,
  journal =      or,
  year =         1983,
  pages =        {938--945},
  anote =        {makespan optimization},
}

@Article{BalBea2008,
  title =        {Facility location in humanitarian relief},
  author =       {Balcik, Burcu and Beamon, Benita M.},
  journal =      ijl,
  volume =       11,
  number =       2,
  pages =        {101--121},
  year =         2008,
  publisher =    taylorfrancis
}

@Article{BalBirStuDor2009ejor,
  author =       Balaprakash #and# Birattari #and# Stuetzle #and# Dorigo,
  title =        {Adaptive Sampling Size and Importance Sampling in Estimation-based
Local Search for the Probabilistic Traveling Salesman Problem},
  journal =      ejor,
  year =         2009,
  volume =       199,
  number =       1,
  pages =        {98--110}
}

@Article{BalBirStuDor2010cor,
  author =       Balaprakash #and# Birattari #and# Stuetzle #and# Dorigo,
  title =        {Estimation-based Metaheuristics for the Probabilistic Travelling Salesman Problem},
  journal =      cor,
  year =         2010,
  volume =       37,
  number =       11,
  pages =        {1939--1951},
  doi =          {10.1016/j.cor.2009.12.005},
}

@Article{BalBirStuDor2015coa,
  author =       Balaprakash #and# Birattari #and# Stuetzle #and# Dorigo,
  title =        {Estimation-based Metaheuristics for the Single Vehicle Routing Problem with Stochastic Demands and Customers},
  journal =      coa,
  year =         2015,
  volume =       61,
  number =       2,
  pages =        {463--487},
  doi =          {10.1007/s10589-014-9719-z},
}

@Article{BalBirStuYuaDor09,
  author =       Balaprakash #and# Birattari #and# Stuetzle #and#
                  Yuan_Z #and# Dorigo,
  title =        {Estimation-based Ant Colony Optimization Algorithms
                  for the Probabilistic Travelling Salesman Problem},
  journal =      swarm,
  volume =       3,
  number =       3,
  year =         2009,
  pages =        {223--242}
}

@Article{BalCar1996,
  author =       Balas #and# {M. C. Carrera},
  title =        {A Dynamic Subgradient-based Branch and Bound
                  Procedure for Set Covering},
  journal =      or,
  year =         1996,
  volume =       44,
  number =       6,
  pages =        {875--890}
}

@Article{BalMar1980,
  author =       Balas #and# {C. Martin},
  title =        {Pivot and Complement--A Heuristic for 0--1 Programming},
  journal =      ms,
  year =         1980,
  volume =       26,
  number =       1,
  pages =        {86--96}
}

@Article{BalPad1976,
  author =       Balas #and# {M. W. Padberg},
  title =        {Set Partitioning: A Survey},
  journal =      siamr,
  year =         1976,
  volume =       18,
  pages =        {710--760}
}

@Article{BalSim01tsptw,
  author =       Balas #and# Simonetti,
  title =        "Linear Time Dynamic-Programming Algorithms for New
                  Classes of Restricted {TSP}s: {A} Computational Study",
  volume =       13,
  doi =         {10.1287/ijoc.13.1.56.9748},
  abstract =     {Consider the following restricted (symmetric or
                  asymmetric) traveling-salesman problem {(TSP):}
                  given an initial ordering of the n cities and an
                  integer $k > 0$, find a minimum-cost
                  feasible tour, where a feasible tour is one in which
                  city $i$ precedes city $j$ whenever $j >= i + k$ in the
                  initial ordering. Balas (1996) has proposed a
                  dynamic-programming algorithm that solves this
                  problem in time linear in n, though exponential in
                  k. Some important real-world problems are amenable
                  to this model or some of its close relatives. The
                  algorithm of Balas (1996) constructs a layered
                  network with a layer of nodes for each position in
                  the tour, such that source-sink paths in this
                  network are in one-to-one correspondence with tours
                  that satisfy the postulated precedence
                  constraints. In this paper we discuss an
                  implementation of the dynamic-programming algorithm
                  for the general case when the integer k is replaced
                  with city-specific integers k(j), j = 1, . . .,
                  n. We discuss applications to, and computational
                  experience with, {TSPs} with time windows, a model
                  frequently used in vehicle routing as well as in
                  scheduling with setup, release and delivery
                  times. We also introduce a new model, the {TSP} with
                  target times, applicable to {Just-in-Time}
                  scheduling problems. Finally for {TSPs} that have no
                  precedence restrictions, we use the algorithm as a
                  heuristic that finds in linear time a local optimum
                  over an exponential-size neighborhood. For this
                  case, we implement an iterated version of our
                  procedure, based on contracting some arcs of the
                  tour produced by a first application of the
                  algorithm, then reapplying the algorithm to the
                  shrunken graph with the same k.},
  number =       1,
  journal =      informsjoc,
  year =         2001,
  keywords =     {tsptw},
  pages =        {56--75}
}

@Article{BalVaz1998,
  author =       Balas #and# {A. Vazacopoulos},
  title =        {Guided Local Search with Shifting Bottleneck for Job
                  Shop Scheduling},
  journal =      ms,
  year =         1998,
  volume =       44,
  number =       2,
  pages =        {262--275}
}

@Article{Bankes2002,
  title =        {Tools and techniques for developing policies for complex and
                  uncertain systems},
  author =       Bankes_S,
  volume =       99,
  number =       {suppl 3},
  pages =        {7263--7266},
  year =         2002,
  abstract =     {Agent-based models (ABM) are examples of complex adaptive
                  systems, which can be characterized as those systems for
                  which no model less complex than the system itself can
                  accurately predict in detail how the system will behave at
                  future times. Consequently, the standard tools of policy
                  analysis, based as they are on devising policies that perform
                  well on some best estimate model of the system, cannot be
                  reliably used for ABM. This paper argues that policy analysis
                  by using ABM requires an alternative approach to decision
                  theory. The general characteristics of such an approach are
                  described, and examples are provided of its application to
                  policy analysis.ABM, agent-based model},
  journal =      pnas,
  doi =          "10.1073/pnas.092081399"
}

@Article{BarBatSenSil2015ijem,
  title =        {Improving the Performance of Metaheuristics: An Approach
                  Combining Response Surface Methodology and Racing Algorithms},
  author =       {Eduardo Batista de Moraes Barbosa and Edson Luiz
                  Franc{\c{c}}a Senne and Messias Borges Silva},
  journal =      ijengmath,
  year =         2015,
  volume =       2015,
  pages =        {Article ID 167031},
  doi =          {10.1155/2015/167031},
  keywords =     {F-race},
}

@Article{BarDiaSerBen2020xai,
  doi =          {10.1016/j.inffus.2019.12.012},
  year =         2020,
  month =        jun,
  publisher =    {Elsevier {BV}},
  volume =       58,
  pages =        {82--115},
  author =       {Alejandro Barredo Arrieta and Natalia
                  D{\'{i}}az-Rodr{\'{i}}guez and Javier Del Ser and Adrien
                  Bennetot and Siham Tabik and Alberto Barbado and Salvador
                  Garcia and Sergio Gil-Lopez and Daniel Molina and Richard
                  Benjamins and Raja Chatila and Francisco Herrera},
  title =        {Explainable Artificial Intelligence ({XAI}): Concepts,
                  taxonomies, opportunities and challenges toward responsible
                  {AI}},
  journal =      inf-fusion,
}

@Article{BarDoeBer2020benchmarking,
  title =        {Benchmarking in Optimization: Best Practice and Open Issues},
  author =       Bartz-Beielstein #and# Doerr_C #and# {Daan van den Berg} #and#
                  Bossek_J #and# {Sowmya Chandrasekaran} #and# Eftimov_T #and#
                  {Andreas Fischbach} #and# Kerschke_P #and# {William {La
                  Cava}} #and# Lopez-Ibanez #and# { Katherine M. Malan} #and#
                  {Jason H. Moore} #and# Naujoks #and# {Patryk Orzechowski}
                  #and# {Vanessa Volz} #and# Wagner_M #and# {Thomas Weise},
  year =         2020,
  journal =      arxiv # {2007.03488 [cs.NE]},
  url =          {https://arxiv.org/abs/2007.03488},
}

@Article{BarGolKel1995,
  author =       {Richard S. Barr and Bruce L. Golden and James P. Kelly} #and# Resende
                  #and# {William R. Stewart, Jr.},
  title =        {Designing and Reporting on Computational Experiments with
                  Heuristic Methods},
  journal =      joh,
  year =         1995,
  volume =       1,
  number =       1,
  pages =        {9--32},
  doi =          {10.1007/BF02430363}
}

@Article{BarJohNem1998or,
  title =        {Branch-and-price: Column generation for solving huge integer
                  programs},
  author =       {Barnhart, Cynthia and Johnson, Ellis L. and Nemhauser, George
                  L.} #and# Savelsbergh_MWP #and# {Vance, Pamela H.},
  journal =      or,
  volume =       46,
  number =       3,
  pages =        {316--329},
  year =         1998
}

@Article{BarKwa2020,
  title =        {On considering robustness in the search phase of Robust
                  Decision Making: A comparison of Many-Objective Robust
                  Decision Making, multi-scenario Many-Objective Robust
                  Decision Making, and Many Objective Robust Optimization},
  author =       {Bartholomew, Erin and Kwakkel, Jan H.},
  journal =      ems,
  pages =        104699,
  year =         2020,
  publisher =    {Elsevier},
  volume =       127,
  doi =          "10.1016/j.envsoft.2020.104699"
}

@Article{BarPea2016,
	author =       Bareinboim_E #and# Pearl_J,
	title =        {Causal inference and the data-fusion problem},
	volume =       113,
	number =       27,
	pages =        {7345--7352},
	year =         2016,
	doi =          {10.1073/pnas.1510507113},
	journal =      pnas
}

@Article{BarZae2017model,
  author =       Bartz-Beielstein #and# Zaefferer,
  title =        {Model-based methods for continuous and discrete global
                  optimization},
  doi =          {10.1016/j.asoc.2017.01.039},
  year =         2017,
  month =        jun,
  volume =       55,
  pages =        {154--167},
  journal =      asoco,
}

@Article{BasFra1990,
  author =       {Basu, Atanu and Frazer, L. Neil},
  title =        {Rapid Determination of the Critical Temperature in Simulated Annealing Inversion},
  journal =      science,
  year =         1990,
  volume =       249,
  number =       4975,
  pages =        {1409--1412}
}

@Article{BatPas2010tec,
  author =       Battiti #and# Passerini_A,
  title =        {Brain-Computer Evolutionary Multiobjective Optimization: A
                  Genetic Algorithm Adapting to the Decision Maker},
  journal =      tec,
  volume =       14,
  number =       5,
  year =         2010,
  pages =        {671--687},
  doi =          {10.1109/TEVC.2010.2058118},
  keywords =     {BC-EMOA},
  annote =       {Errata: DTLZ6 and DTLZ7 in the paper are actually DTLZ7 and
                  DTLZ8 in \cite{DebThiLau2005dtlz}}
}

@Article{BatPro1996:jea,
  author =       Battiti #and# {M. Protasi},
  title =        {Reactive Search, A History-Based Heuristic for
                  {MAX}-{SAT}},
  journal =      jea,
  year =         1997,
  volume =       2,
}

@Article{BatSchUrl2017,
  author =       {Michele Battistutta} #and# Schaerf #and# Urli,
  title =        {Feature-based Tuning of Single-stage Simulated Annealing for
                  Examination Timetabling},
  journal =      aor,
  year =         2017,
  volume =       252,
  number =       2,
  pages =        {239--254}
}

@Article{BatTec1994:cma,
  title =        {Simulated annealing and Tabu search in the long run: A
                  comparison on {QAP} tasks},
  author =       Battiti #and# Tecchiolli_G,
  journal =      cma,
  volume =       28,
  number =       6,
  pages =        {1--8},
  year =         1994,
  publisher =    elsevier,
  doi =          "10.1016/0898-1221(94)00147-2"
}

@Article{BatTec1994:orsa,
  author =       Battiti #and# Tecchiolli_G,
  title =        {The Reactive Tabu Search},
  journal =      orsa,
  year =         1994,
  volume =       6,
  number =       2,
  pages =        {126--140},
}

@Article{BatTec1996aor,
  author       = Battiti #and# Tecchiolli_G,
  title        = {The continuous reactive tabu search: blending combinatorial
                  optimization and stochastic search for global optimization},
  journal      = aor,
  year         = 1996,
  volume       = 63,
  number       = 2,
  pages        = {151--188},
  doi          = {10.1007/BF02125453}
}

@Article{BauPer2007:ejor,
  author =       Bautista #and# Pereira_J,
  title =        {Ant algorithms for a time and space constrained
                  assembly line balancing problem},
  journal =      ejor,
  year =         2007,
  volume =       177,
  number =       3,
  pages =        {2016--2032},
  doi =          {10.1016/j.ejor.2005.12.017},
}

@Article{Baumol1962,
  author =       {Baumol, William J.},
  title =        {Management models and industrial applications of linear
                  programming},
  journal =      nrlq,
  volume =       9,
  number =       1,
  doi =          {10.1002/nav.3800090109},
  pages =        {63--64},
  year =         1962
}

@Article{Baxter1981,
  author =       {John Baxter},
  title =        {Local Optima Avoidance in Depot Location},
  journal =      jors,
  year =         1981,
  volume =       32,
  number =       9,
  pages =        {815--819}
}

@Article{BeaChu1996,
  author = 	 Beasley #and# {P. C. Chu},
  title = 	 {A Genetic Algorithm for the Set Covering Problem},
  journal =	 ejor,
  year =	 1996,
  volume =	 94,
  number =	 2,
  pages =	 {392--404}
}

@Article{BeaChu1998,
  author = 	 Beasley #and# {P. C. Chu},
  title = 	 {A Genetic Algorithm for the Multidimensional Knapsack Problem},
  journal = 	 joh,
  year = 	 1998,
  volume = 	 4,
  number = 	 1,
  pages = 	 {63--86}
}

@Article{BeaShaSmiLop2018review,
  author =       {Bealt, Jennifer and Shaw, Duncan and Smith, Chris M.} #and#
                  Lopez-Ibanez,
  year =         2019,
  title =        {Peer Reviews for Making Cities Resilient: A Systematic
                  Literature Review},
  journal =      ijemgmt,
  volume =       15,
  number =       4,
  pages =        {334--359},
  doi =          "10.1504/IJEM.2019.104201",
  abstract =     "Peer reviews are a unique governance tool that use expertise
                  from one city or country to assess and strengthen the
                  capabilities of another. Peer review tools are gaining
                  momentum in disaster management and remain an important but
                  understudied topic in risk governance. Methodologies to
                  conduct a peer review are still in their infancy. To enhance
                  these, a systematic literature review (SLR) of academic and
                  non-academic literature was conducted on city resilience peer
                  reviews. Thirty-three attributes of resilience are
                  identified, which provides useful insights into how research
                  and practice can inform risk governance, and utilise peer
                  reviews, to drive meaningful change. Moreover, it situates
                  the challenges associated with resilience building tools
                  within risk governance to support the development of
                  interdisciplinary perspectives for integrated city resilience
                  frameworks. Results of this research have been used to
                  develop a peer review methodology and an international
                  standard on conducting peer reviews for disaster risk
                  reduction.",
  keywords =     "city resilience, city peer review, disaster risk governance"
}

@Article{Beasley1990orlib,
  author =       Beasley,
  title =        "{OR}-{Library:} distributing test problems by electronic
                  mail",
  journal =      jors,
  year =         1990,
  pages =        {1069--1072},
  note =         {Currently available from
                  \url{http://people.brunel.ac.uk/~mastjjb/jeb/info.html}},
}

@Article{BehFat2011,
  author = 	 {J. Behnamian and S. M. T. {Fatemi Ghomi}},
  title = 	 {Hybrid Flowshop Scheduling with Machine and Resource-dependent Processing Times},
  journal = 	 amm,
  year = 	 2011,
  volume = 	 35,
  number = 	 3,
  pages = 	 {1107--1123}
}

@Article{Bel1954,
  author  = {Richard Bellman},
  title   = {The theory of dynamic programming},
  journal = {Bulletin of the American Mathematical Society},
  volume  = 60,
  year    = 1954,
  pages   = {503--515}
}

@Article{BelCesDigSchUrl2016,
  author =       {Ruggero Bellio} #and# Ceschia_S #and# DiGaspero #and# Schaerf #and# Urli,
  title =        {Feature-based tuning of simulated annealing applied to
                  the curriculum-based course timetabling problem},
  journal =      cor,
  volume =       65,
  pages =        {83--92},
  year =         2016,
  publisher =    elsevier
}

@Article{Ben92,
  author =       Bentley,
  title =        {Fast Algorithms for Geometric Traveling Salesman
                  Problems},
  journal =      orsa,
  year =         1992,
  volume =       4,
  number =       4,
  pages =        {387--411}
}

@Article{BenKao2013,
  author =       {Una Benlic} #and# Hao_JK,
  title =        {Breakout Local Search for the Quadratic Assignment Problem},
  journal =      amc,
  year =         2013,
  volume =       219,
  number =       9,
  pages =        {4800--4815}
}

@Article{BenLiuAuIst2014transient,
  title =        {Transient protein-protein interface prediction: datasets,
                  features, algorithms, and the {RAD-T} predictor},
  author =       {Bendell, Calem J. and Liu, Shalon and Aumentado-Armstrong,
                  Tristan and Istrate, Bogdan and Cernek, Paul T. and Khan,
                  Samuel and Picioreanu, Sergiu and Zhao, Michael and Murgita,
                  Robert A.},
  journal =      bmc-bioinf,
  volume =       15,
  pages =        82,
  year =         2014,
}

@Article{BenLodPro2021ml,
  author       = Bengio_Y #and# Lodi #and# {Antoine Prouvost},
  title        = {Machine learning for combinatorial optimization: A
                  methodological tour d'horizon},
  journal      = ejor,
  year         = 2021,
  volume       = 290,
  number       = 2,
  pages        = {405--421},
  doi          = {10.1016/j.ejor.2020.07.063},
  keywords     = {Combinatorial optimization, Machine learning, Branch and
                  bound, Mixed-integer programming solvers},
  abstract     = {This paper surveys the recent attempts, both from the machine
                  learning and operations research communities, at leveraging
                  machine learning to solve combinatorial optimization
                  problems. Given the hard nature of these problems,
                  state-of-the-art algorithms rely on handcrafted heuristics
                  for making decisions that are otherwise too expensive to
                  compute or mathematically not well defined. Thus, machine
                  learning looks like a natural candidate to make such
                  decisions in a more principled and optimized way. We advocate
                  for pushing further the integration of machine learning and
                  combinatorial optimization and detail a methodology to do
                  so. A main point of the paper is seeing generic optimization
                  problems as data points and inquiring what is the relevant
                  distribution of problems to use for learning on a given
                  task.}
}

@Article{BenRit2016:cor,
  author =       {Alexander Javier Benavides} #and# Ritt,
  title =        {Two Simple and Effective Heuristics for Minimizing the
                  Makespan in Non-permutation Flow Shops},
  journal =      cor,
  year =         2016,
  volume =       66,
  pages =        {160--169},
  doi =          {10.1016/j.cor.2015.08.001}
}

@Article{Benders1962,
  author = 	 {Benders, J. F.},
  title = 	 {Partitioning Procedures for Solving Mixed-variables Programming Problems},
  journal = 	 nm,
  year = 	 1962,
  volume = 	 4,
  number = 	 3,
  pages = 	 {238--252}
}

@Article{Bentley1980,
  author =       Bentley,
  title =        {Multidimensional Divide-and-conquer},
  journal =      cacm,
  year =         1980,
  volume =       23,
  number =       4,
  doi =          "10.1145/358841.358850",
  pages =        {214--229},
  abstract =     "Most results in the field of algorithm design are single
                  algorithms that solve single problems. In this paper we
                  discuss multidimensional divide-and-conquer, an algorithmic
                  paradigm that can be instantiated in many different ways to
                  yield a number of algorithms and data structures for
                  multidimensional problems. We use this paradigm to give
                  best-known solutions to such problems as the ECDF, maxima,
                  range searching, closest pair, and all nearest neighbor
                  problems. The contributions of the paper are on two
                  levels. On the first level are the particular algorithms and
                  data structures given by applying the paradigm.  On the
                  second level is the more novel contribution of this paper: a
                  detailed study of an algorithmic paradigm that is specific
                  enough to be described precisely yet general enough to solve
                  a wide variety of problems."
}

@Article{BerBen2012jmlr,
  author =       Bergstra #and# Bengio_Y,
  title =        {Random Search for Hyper-Parameter Optimization},
  journal =      jmlr,
  year =         2012,
  volume =       13,
  pages =        {281--305},
  abstract =     {Grid search and manual search are the most widely
                  used strategies for hyper-parameter
                  optimization. This paper shows empirically and
                  theoretically that randomly chosen trials are more
                  efficient for hyper-parameter optimization than
                  trials on a grid. Empirical evidence comes from a
                  comparison with a large previous study that used
                  grid search and manual search to configure neural
                  networks and deep belief networks. Compared with
                  neural networks configured by a pure grid search, we
                  find that random search over the same domain is able
                  to find models that are as good or better within a
                  small fraction of the computation time. Granting
                  random search the same computational budget, random
                  search finds better models by effectively searching
                  a larger, less promising configuration
                  space. Compared with deep belief networks configured
                  by a thoughtful combination of manual search and
                  grid search, purely random search over the same
                  32-dimensional configuration space found
                  statistically equal performance on four of seven
                  data sets, and superior performance on one of
                  seven. A Gaussian process analysis of the function
                  from hyper-parameters to validation set performance
                  reveals that for most data sets only a few of the
                  hyper-parameters really matter, but that different
                  hyper-parameters are important on different data
                  sets. This phenomenon makes grid search a poor
                  choice for configuring algorithms for new data
                  sets. Our analysis casts some light on why recent
                  "High Throughput" methods achieve surprising
                  success: they appear to search through a large number
                  of hyper-parameters because most hyper-parameters do
                  not matter much. We anticipate that growing interest
                  in large hierarchical models will place an
                  increasing burden on techniques for hyper-parameter
                  optimization; this work shows that random search is
                  a natural baseline against which to judge progress
                  in the development of adaptive (sequential)
                  hyper-parameter optimization algorithms.},
  epub =
                  {http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf}
}

@Article{BerEmmTav2017managing,
  title =        {Managing catastrophic climate risks under model uncertainty
                  aversion},
  author =       {Berger, Lo{\"i}c and Emmerling, Johannes and Tavoni,
                  Massimo},
  journal =      ms,
  volume =       63,
  number =       3,
  pages =        {749--765},
  year =         2017,
  publisher =    informs
}

@Article{BerFisLod2007,
  title =        {A feasibility pump heuristic for general mixed-integer problems},
  author =       {Bertacco, Livio} #and# Fischetti #and# Lodi,
  journal =      do,
  volume =       4,
  number =       1,
  pages =        {63--76},
  year =         2007,
  publisher =    elsevier
}

@Article{BerKal2020,
  title =        {From predictive to prescriptive analytics},
  author =       {Bertsimas, Dimitris and Kallus, Nathan},
  journal =      ms,
  volume =       66,
  number =       3,
  pages =        {1025--1044},
  year =         2020,
  publisher =    informs
}

@Article{BerKraSch2016bayesian,
  author       = {Felix Berkenkamp and Andreas Krause and Angela P. Schoellig},
  title        = {Bayesian Optimization with Safety Constraints: Safe and
                  Automatic Parameter Tuning in Robotics},
  journal      = arxiv # {1602.04450},
  year         = 2016,
  url          = {http://arxiv.org/abs/1602.04450},
  keywords     = {Safe Optimization, SafeOpt}
}

@Article{BerKraSch2021bayesian,
  author       = {Berkenkamp, Felix and Krause, Andreas and Schoellig, Angela
                  P.},
  title        = {Bayesian optimization with safety constraints: safe and
                  automatic parameter tuning in robotics},
  journal      = ml,
  year         = 2021,
  month        = jun,
  annote       = {Preprint: \url{http://arxiv.org/abs/1602.04450}},
  doi          = {10.1007/s10994-021-06019-1},
  abstract     = {Selecting the right tuning parameters for algorithms is a
                  pravelent problem in machine learning that can significantly
                  affect the performance of algorithms. Data-efficient
                  optimization algorithms, such as Bayesian optimization, have
                  been used to automate this process. During experiments on
                  real-world systems such as robotic platforms these methods
                  can evaluate unsafe parameters that lead to safety-critical
                  system failures and can destroy the system. Recently, a safe
                  Bayesian optimization algorithm, called SafeOpt, has been
                  developed, which guarantees that the performance of the
                  system never falls below a critical value; that is, safety is
                  defined based on the performance function. However, coupling
                  performance and safety is often not desirable in practice,
                  since they are often opposing objectives. In this paper, we
                  present a generalized algorithm that allows for multiple
                  safety constraints separate from the objective. Given an
                  initial set of safe parameters, the algorithm maximizes
                  performance but only evaluates parameters that satisfy safety
                  for all constraints with high probability. To this end, it
                  carefully explores the parameter space by exploiting
                  regularity assumptions in terms of a Gaussian process
                  prior. Moreover, we show how context variables can be used to
                  safely transfer knowledge to new situations and tasks. We
                  provide a theoretical analysis and demonstrate that the
                  proposed algorithm enables fast, automatic, and safe
                  optimization of tuning parameters in experiments on a
                  quadrotor vehicle.}
}

@Article{BerTsiWu1997:joh,
  author =       {Dimitri P. Bertsekas and John N. Tsitsiklis and Cynara Wu},
  title =        {Rollout Algorithms for Combinatorial Optimization},
  journal =      joh,
  year =         1997,
  volume =       3,
  number =       3,
  pages =        {245--262}
}

@Article{BerWan1987binpack,
  title =        {Two-dimensional finite bin-packing algorithms},
  author =       {Berkey, Judith O. and Wang, Pearl Y.},
  journal =      jors,
  volume =       38,
  number =       5,
  pages =        {423--429},
  year =         1987,
  doi =          {10.2307/2582731}
}

@Article{BeuFonLopPaqVah09:tec,
  author =       Beume #and# Fonseca #and# Lopez-Ibanez #and# Paquete
                  #and# Vahrenhold,
  title =        {On the complexity of computing the hypervolume
                  indicator},
  journal =      tec,
  year =         2009,
  volume =       13,
  number =       5,
  pages =        {1075--1082},
  doi =          {10.1109/TEVC.2009.2015575},
  abstract =     {The goal of multi-objective optimization is to find
                  a set of best compromise solutions for typically
                  conflicting objectives. Due to the complex nature of
                  most real-life problems, only an approximation to
                  such an optimal set can be obtained within
                  reasonable (computing) time. To compare such
                  approximations, and thereby the performance of
                  multi-objective optimizers providing them, unary
                  quality measures are usually applied. Among these,
                  the \emph{hypervolume indicator} (or
                  \emph{S-metric}) is of particular relevance due to
                  its favorable properties. Moreover, this indicator
                  has been successfully integrated into stochastic
                  optimizers, such as evolutionary algorithms, where
                  it serves as a guidance criterion for finding good
                  approximations to the Pareto front. Recent results
                  show that computing the hypervolume indicator can be
                  seen as solving a specialized version of Klee's
                  Measure Problem.  In general, Klee's Measure Problem
                  can be solved with $\mathcal{O}(n \log n +
                  n^{d/2}\log n)$ comparisons for an input instance of
                  size $n$ in $d$ dimensions; as of this writing, it
                  is unknown whether a lower bound higher than
                  $\Omega(n \log n)$ can be proven. In this article,
                  we derive a lower bound of $\Omega(n\log n)$ for the
                  complexity of computing the hypervolume indicator in
                  any number of dimensions $d>1$ by reducing the
                  so-called \textsc{UniformGap} problem to it.  For
                  the three dimensional case, we also present a
                  matching upper bound of $\mathcal{O}(n\log n)$
                  comparisons that is obtained by extending an
                  algorithm for finding the maxima of a point set.},
}

@Article{BeuNauEmm2007ejor,
  author =       Beume #and# Naujoks #and# Emmerich,
  title =        "{SMS-EMOA}: Multiobjective selection based on
                  dominated hypervolume",
  journal =      ejor,
  year =         2007,
  volume =       181,
  number =       3,
  pages =        {1653--1669},
  doi =          {10.1016/j.ejor.2006.08.008},
}

@Article{BeySch2002:es,
  author =       Beyer_HG #and# Schwefel,
  title =        {Evolution Strategies: A Comprehensive Introduction},
  journal =      natcomp,
  volume =       1,
  pages =        {3--52},
  year =         2002
}

@Article{BeySchWeg2002,
  title =        {How to analyse evolutionary algorithms},
  author =       Beyer_HG #and# Schwefel #and# Wegener,
  journal =      tcs,
  volume =       287,
  number =       1,
  pages =        {101--130},
  year =         2002,
  publisher =    elsevier
}

@Article{BezLopStu2015tec,
  author       = Bezerra #and# Lopez-Ibanez #and# Stuetzle,
  title        = {Automatic Component-Wise Design of Multi-Objective
                  Evolutionary Algorithms},
  journal      = tec,
  year         = 2016,
  volume       = 20,
  number       = 3,
  pages        = {403--417},
  doi          = {10.1109/TEVC.2015.2474158},
  supplement   = {https://github.com/iridia-ulb/automoea-tevc-2016},
  pdf          = {BezLopStu2015tec.pdf}
}

@Article{BezLopStu2017assessment,
  author =       Bezerra #and# Lopez-Ibanez #and# Stuetzle,
  title =        "A Large-Scale Experimental Evaluation of High-Performing
                  Multi- and Many-Objective Evolutionary Algorithms",
  year =         2018,
  journal =      ec,
  doi =          {10.1162/evco_a_00217},
  supplement =   {http://iridia.ulb.ac.be/supp/IridiaSupp2015-007/},
  pdf =          "BezLopStu2017assessment.pdf",
  volume =       26,
  number =       4,
  pages =        {621--656},
  alias =        "BezLopStu2016assessment",
  abstract =     {Research on multi-objective evolutionary algorithms (MOEAs)
                  has produced over the past decades a large number of
                  algorithms and a rich literature on performance assessment
                  tools to evaluate and compare them. Yet, newly proposed MOEAs
                  are typically compared against very few, often a decade older
                  MOEAs. One reason for this apparent contradiction is the lack
                  of a common baseline for comparison, with each subsequent
                  study often devising its own experimental scenario, slightly
                  different from other studies. As a result, the state of the
                  art in MOEAs is a disputed topic. This article reports a
                  systematic, comprehensive evaluation of a large number of
                  MOEAs that covers a wide range of experimental scenarios. A
                  novelty of this study is the separation between the
                  higher-level algorithmic components related to
                  multi-objective optimization (MO), which characterize each
                  particular MOEA, and the underlying parameters-such as
                  evolutionary operators, population size, etc.-whose
                  configuration may be tuned for each scenario. Instead of
                  relying on a common or "default" parameter configuration that
                  may be low-performing for particular MOEAs or scenarios and
                  unintentionally biased, we tune the parameters of each MOEA
                  for each scenario using automatic algorithm configuration
                  methods. Our results confirm some of the assumed knowledge in
                  the field, while at the same time they provide new insights
                  on the relative performance of MOEAs for many-objective
                  problems. For example, under certain conditions,
                  indicator-based MOEAs are more competitive for such problems
                  than previously assumed. We also analyze problem-specific
                  features affecting performance, the agreement between
                  performance metrics, and the improvement of tuned
                  configurations over the default configurations used in the
                  literature. Finally, the data produced is made publicly
                  available to motivate further analysis and a baseline for
                  future comparisons.}
}

@Article{BezLopStu2019ec,
  author =       Bezerra #and# Lopez-Ibanez #and# Stuetzle,
  title =        "Automatically Designing State-of-the-Art Multi- and
                  Many-Objective Evolutionary Algorithms",
  journal =      ec,
  year =         2020,
  volume =       28,
  number =       2,
  pages =        "195--226",
  doi =          "10.1162/evco_a_00263",
  supplement =   {https://github.com/iridia-ulb/automoea-ecj-2020},
  pdf =          "BezLopStu2019ec.pdf",
  abstract =     "A recent comparison of well-established multiobjective
                  evolutionary algorithms (MOEAs) has helped better identify
                  the current state-of-the-art by considering (i) parameter
                  tuning through automatic configuration, (ii) a wide range of
                  different setups, and (iii) various performance
                  metrics. Here, we automatically devise MOEAs with verified
                  state-of-the-art performance for multi- and many-objective
                  continuous optimization. Our work is based on two main
                  considerations. The first is that high-performing algorithms
                  can be obtained from a configurable algorithmic framework in
                  an automated way. The second is that multiple performance
                  metrics may be required to guide this automatic design
                  process. In the first part of this work, we extend our
                  previously proposed algorithmic framework, increasing the
                  number of MOEAs, underlying evolutionary algorithms, and
                  search paradigms that it comprises. These components can be
                  combined following a general MOEA template, and an automatic
                  configuration method is used to instantiate high-performing
                  MOEA designs that optimize a given performance metric and
                  present state-of-the-art performance. In the second part, we
                  propose a multiobjective formulation for the automatic MOEA
                  design, which proves critical for the context of
                  many-objective optimization due to the disagreement of
                  established performance metrics. Our proposed formulation
                  leads to an automatically designed MOEA that presents
                  state-of-the-art performance according to a set of metrics,
                  rather than a single one."
}

@Article{BiaBirMan2006jmma,
  author =       Bianchi_L #and# Birattari #and# " M. Manfrin and
                  M. Mastrolilli "#and# Paquete #and# Rossi-Doria #and#
                  Schiavinotto,
  title =        {Hybrid Metaheuristics for the Vehicle Routing Problem with
                  Stochastic Demands},
  journal =      jmma,
  year =         2006,
  volume =       5,
  number =       1,
  pages =        {91--110},
  alias =        {Bia++06}
}

@Article{BiaDorGam2009survey,
  title =        {A survey on metaheuristics for stochastic combinatorial
                  optimization},
  author =       Bianchi_L #and# Dorigo #and# Gambardella #and# Gutjahr,
  journal =      natcomp,
  volume =       8,
  number =       2,
  pages =        {239--287},
  year =         2009,
}

@Article{BinGinRou2015gaupar,
  title =        {Quantifying uncertainty on {Pareto} fronts with {Gaussian}
                  process conditional simulations},
  volume =       243,
  doi =          {10.1016/j.ejor.2014.07.032},
  abstract =     {Multi-objective optimization algorithms aim at finding
                  Pareto-optimal solutions. Recovering Pareto fronts or Pareto
                  sets from a limited number of function evaluations are
                  challenging problems. A popular approach in the case of
                  expensive-to-evaluate functions is to appeal to
                  metamodels. Kriging has been shown efficient as a base for
                  sequential multi-objective optimization, notably through
                  infill sampling criteria balancing exploitation and
                  exploration such as the Expected Hypervolume
                  Improvement. Here we consider Kriging metamodels not only for
                  selecting new points, but as a tool for estimating the whole
                  Pareto front and quantifying how much uncertainty remains on
                  it at any stage of Kriging-based multi-objective optimization
                  algorithms. Our approach relies on the Gaussian process
                  interpretation of Kriging, and bases upon conditional
                  simulations. Using concepts from random set theory, we
                  propose to adapt the Vorob'ev expectation and deviation to
                  capture the variability of the set of non-dominated
                  points. Numerical experiments illustrate the potential of the
                  proposed workflow, and it is shown on examples how Gaussian
                  process simulations and the estimated Vorob'ev deviation can
                  be used to monitor the ability of Kriging-based
                  multi-objective optimization algorithms to accurately learn
                  the Pareto front.},
  number =       2,
  journal =      ejor,
  author =       {Binois, M. and Ginsbourger, D. and Roustant, O.},
  year =         2015,
  keywords =     {Attainment function, Expected Hypervolume Improvement,
                  Kriging, Multi-objective optimization, Vorob'ev expectation},
  pages =        {386--394},
}

@Article{BirBalStuDor07:informs,
  author =       Birattari #and# Balaprakash #and# Stuetzle #and# Dorigo,
  title =        {Estimation Based Local Search for Stochastic Combinatorial Optimization},
  journal =      informsjoc,
  year =         2008,
  volume =       20,
  number =       4,
  pages =        {644--658}
}

@Article{BirPelDor2007:tec,
  author =       Birattari #and# Pellegrini #and# Dorigo,
  title =        {On the invariance of ant colony optimization},
  journal =      tec,
  year =         2007,
  volume =       11,
  number =       6,
  pages =        {732--742},
  doi =          {10.1109/TEVC.2007.892762},
  alias =        {BirPelDor2007ieee-tevc},
  pdf =          {BirPelDor2007ieee-tevc.pdf},
}

@Article{BirZloDor06meta_design,
  author =       Birattari #and# {Zlochin, M.} #and# Dorigo,
  title =        {Towards a theory of practice in metaheuristics design: A machine learning perspective},
  journal =      tia,
  year =         2006,
  volume =       40,
  number =       2,
  pages =        {353--369},
}

@Article{BisIzzYam2010:pagmo-arxiv,
  title =        {A Global Optimisation Toolbox for Massively Parallel
                  Engineering Optimisation},
  author =       {Biscani, Francesco} #and# Izzo_D #and# {Yam, Chit Hong},
  journal =      arxiv # {1004.3824},
  year =         2010,
  url =          {http://arxiv.org/abs/1004.3824},
  keywords =     {PaGMO},
  abstract =     {A software platform for global optimisation, called PaGMO,
                  has been developed within the Advanced Concepts Team (ACT) at
                  the European Space Agency, and was recently released as an
                  open-source project. PaGMO is built to tackle
                  high-dimensional global optimisation problems, and it has
                  been successfully used to find solutions to real-life
                  engineering problems among which the preliminary design of
                  interplanetary spacecraft trajectories - both chemical
                  (including multiple flybys and deep-space maneuvers) and
                  low-thrust (limited, at the moment, to single phase
                  trajectories), the inverse design of nano-structured
                  radiators and the design of non-reactive controllers for
                  planetary rovers. Featuring an arsenal of global and local
                  optimisation algorithms (including genetic algorithms,
                  differential evolution, simulated annealing, particle swarm
                  optimisation, compass search, improved harmony search, and
                  various interfaces to libraries for local optimisation such
                  as SNOPT, IPOPT, GSL and NLopt), PaGMO is at its core a C++
                  library which employs an object-oriented architecture
                  providing a clean and easily-extensible optimisation
                  framework. Adoption of multi-threaded programming ensures the
                  efficient exploitation of modern multi-core architectures and
                  allows for a straightforward implementation of the island
                  model paradigm, in which multiple populations of candidate
                  solutions asynchronously exchange information in order to
                  speed-up and improve the optimisation process. In addition to
                  the C++ interface, PaGMO's capabilities are exposed to the
                  high-level language Python, so that it is possible to easily
                  use PaGMO in an interactive session and take advantage of the
                  numerous scientific Python libraries available.}
}

@Article{BisBinLan2023wirdmkd,
  title =         {Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges},
  author =        Bischl_B #and# {Binder, Martin and Lang, Michel and Pielok, Tobias and Richter, Jakob and Coors, Stefan and Thomas, Janek and Ullmann, Theresa and Becker, Marc and Boulesteix, Anne-Laure and Deng, Difan} #and# Lindauer_M,
  journal =       wirdmkd,
  volume =        13,
  number =        2,
  pages =         {e1484},
  year =          2023,
  publisher =     wol
}

@Article{BisKerKot++16:ASlib,
  author       = Bischl_B #and# Kerschke_P #and# Kotthoff_L #and# Lindauer_M
                  #and# Malitsky_Y #and# {Alexandre Fr{\'{e}}chette} #and# Hoos
                  #and# Hutter #and# Leyton-Brown #and# Tierney #and#
                  Vanschoren_J,
  title        = {{ASlib}: A Benchmark Library for Algorithm Selection},
  journal      = aij,
  year         = 2016,
  volume       = 237,
  pages        = {41--58}
}

@Article{BisLanKot2016mlr,
  title =        {{\rpackage{mlr}}: Machine Learning in \proglang{R}},
  author =       Bischl_B #and# {Michel Lang} #and# Kotthoff_L #and# {Julia
                  Schiffner and Jakob Richter and Erich Studerus and Giuseppe
                  Casalicchio and Zachary M. Jones},
  journal =      jmlr,
  year =         2016,
  volume =       17,
  number =       170,
  pages =        {1--5},
  epub =         {http://jmlr.org/papers/v17/15-066.html},
}

@Article{BlaHerSanMar2008vis,
  title =        {A new graphical visualization of n-dimensional {Pareto} front
                  for decision-making in multiobjective optimization},
  author =       {Blasco, Xavier and Herrero, Juan M. and Sanchis, Javier and
                  Mart{\'i}nez, Manuel},
  journal =      is,
  volume =       178,
  number =       20,
  pages =        {3908--3924},
  year =         2008,
  publisher =    {Elsevier}
}

@Article{BlaRayEde2017:corr,
  author =       {Craig Blackmore and Oliver Ray and Kerstin Eder},
  title =        {Automatically Tuning the {GCC} Compiler to Optimize the
                  Performance of Applications Running on Embedded Systems},
  journal =      arxiv # {1703.08228},
  url =          {https://arxiv.org/abs/1703.08228},
  year =         2017
}

@Article{BleBlu2007:jmma,
  author =       Blesa #and# Blum,
  title =        {Finding edge-disjoint paths in networks by means of
                  artificial ant colonies},
  journal =      jmma,
  year =         2007,
  volume =       6,
  number =       3,
  pages =        {361--391}
}

@Article{BliCosRefZha2023aitsp,
  title        = {The First {AI4TSP} Competition: Learning to Solve Stochastic
                  Routing Problems},
  journal      = aij,
  pages        = 103918,
  volume       = 319,
  year         = 2023,
  issn         = {0004-3702},
  doi          = {10.1016/j.artint.2023.103918},
  author       = {Laurens Bliek and Paulo {da Costa} and Reza {Refaei Afshar}
                  and Robbert Reijnen and Yingqian Zhang and Tom Catshoek and
                  Dani{\"e}l Vos and Sicco Verwer and Fynn Schmitt-Ulms and
                  Andr{\'e} Hottung and Tapan Shah} #and# Sellmann #and#
                  Tierney #and# {Carl Perreault-Lafleur and Caroline Leboeuf
                  and Federico Bobbio and Justine Pepin and Warley Almeida
                  Silva and Ricardo Gama and Hugo L. Fernandes} #and# Zaefferer
                  #and# Lopez-Ibanez #and# Irurozki_E,
  keywords     = {AI for TSP competition, Travelling salesman problem, Routing
                  problem, Stochastic combinatorial optimization,
                  Surrogate-based optimization, Deep reinforcement learning},
  abstract     = {This paper reports on the first international competition on
                  AI for the traveling salesman problem (TSP) at the
                  International Joint Conference on Artificial Intelligence
                  2021 (IJCAI-21). The TSP is one of the classical
                  combinatorial optimization problems, with many variants
                  inspired by real-world applications. This first competition
                  asked the participants to develop algorithms to solve an
                  orienteering problem with stochastic weights and time windows
                  (OPSWTW). It focused on two learning approaches:
                  surrogate-based optimization and deep reinforcement
                  learning. In this paper, we describe the problem, the
                  competition setup, and the winning methods, and give an
                  overview of the results. The winning methods described in
                  this work have advanced the state-of-the-art in using AI for
                  stochastic routing problems. Overall, by organizing this
                  competition we have introduced routing problems as an
                  interesting problem setting for AI researchers. The simulator
                  of the problem has been made open-source and can be used by
                  other researchers as a benchmark for new learning-based
                  methods. The instances and code for the competition are
                  available at
                  \url{https://github.com/paulorocosta/ai-for-tsp-competition}.}
}

@Article{Blu05:cor,
  author =       Blum,
  title =        {{Beam-ACO}---{Hybridizing} Ant Colony Optimization
                  with Beam Search: {An} Application to Open Shop
                  Scheduling},
  journal =      cor,
  year =         2005,
  volume =       32,
  number =       6,
  pages =        {1565--1591},
  alias = {BlumCOR05},
}

@Article{Blu08:informs,
  author =       Blum,
  title =        "Beam-{ACO} for simple assembly line balancing",
  journal =      informsjoc,
  year =         2008,
  volume =       20,
  number =       4,
  pages =        {618--627},
  doi =          {10.1287/ijoc.1080.0271},
  alias =        {Blu08:ijoc},
}

@Article{BluBleLop09-BeamSearch-LCS,
  author =       Blum #and# Blesa #and# Lopez-Ibanez,
  title =        {Beam search for the longest common subsequence
                  problem},
  number =       12,
  journal =      cor,
  year =         2009,
  pages =        {3178--3186},
  volume =       36,
  doi =         {10.1016/j.cor.2009.02.005},
  pdf =          {BluBleLop09-BeamSearch-LCS.pdf},
  abstract =     {The longest common subsequence problem is a classical string
                  problem that concerns finding the common part of a set of
                  strings. It has several important applications, for example,
                  pattern recognition or computational biology. Most research
                  efforts up to now have focused on solving this problem
                  optimally. In comparison, only few works exist dealing with
                  heuristic approaches. In this work we present a deterministic
                  beam search algorithm. The results show that our algorithm
                  outperforms the current state-of-the-art approaches not only
                  in solution quality but often also in computation time.},
}

@Article{BluCaBle2015swarm,
  author =       Blum #and# "Borja Calvo" #and# Blesa,
  title =        {{FrogCOL} and {FrogMIS}: New Decentralized Algorithms for Finding Large Independent Sets in Graphs},
  journal =      swarm,
  year =         2015,
  volume =       9,
  number =       {2-3},
  pages =        {205--227},
  doi =          {10.1007/s11721-015-0110-1},
  keywords =     {irace},
}

@Article{BluDor03:ieee_tsmcb,
  author =       Blum #and# Dorigo,
  title =        {The hyper-cube framework for ant colony optimization},
  journal =      tsmcb,
  year =         2004,
  volume =       34,
  number =       2,
  pages =        {1161--1172},
}

@Article{BluDor2005:tec,
  author =       Blum #and# Dorigo,
  journal =      tec,
  number =       2,
  pages =        {159--174},
  title =        {Search Bias in Ant Colony Optimization: On the Role
                  of Competition-Balanced Systems},
  volume =       9,
  year =         2005
}

@Article{BluOch2021,
  author =       Blum #and# Ochoa,
  title =        {A comparative analysis of two matheuristics by means of merged local optima networks},
  journal =      ejor,
  volume =       290,
  number =       1,
  pages =        {36--56},
  year =         2021
}

@Article{BluPinLopLoz2015cor,
  author =       Blum #and# Pinacho #and# Lopez-Ibanez #and# Lozano_JA,
  title =        "Construct, Merge, Solve \& Adapt: A New General Algorithm for
                  Combinatorial Optimization",
  journal =      cor,
  year =         2016,
  volume =       68,
  pages =        "75--88",
  doi =          "10.1016/j.cor.2015.10.014",
  keywords =     {irace, CMSA},
}

@Article{BluPucRaiRol11:asc,
  author =       Blum #and# Puchinger #and# Raidl #and# Roli,
  title =        {Hybrid Metaheuristics in Combinatorial Optimization: A Survey},
  journal =      asoco,
  year =         2011,
  volume =       11,
  number =       6,
  pages =        {4135--4151}
}

@Article{BluRol03:acm-cs,
  author =       Blum #and# Roli,
  title =        {Metaheuristics in Combinatorial Optimization:
                  Overview and Conceptual Comparison},
  journal =      acm-cs,
  year =         2003,
  volume =       35,
  number =       3,
  pages =        {268--308}
}

@Article{BluSam2004:jmma,
  author =       Blum #and# Sampels_M,
  title =        {An Ant Colony Optimization Algorithm for Shop
                  Scheduling Problems},
  journal =      jmma,
  year =         2004,
  volume =       3,
  number =       3,
  pages =        {285--308},
  doi =          {10.1023/B:JMMA.0000038614.39977.6f}
}

@Article{BluYabBle08:cor,
  author =       Blum #and# Yabar_Valles #and# Blesa,
  title =        {An ant colony optimization algorithm for {DNA} sequencing by hybridization},
  journal =      cor,
  year =         2008,
  volume =       35,
  number =       11,
  pages =        {3620--3635},
  alias = {BluYabBle08},
}

@Article{BocFawVal2018performance,
  author =       {Bocchese, Andrea F.} #and# Fawcett #and# {Vallati, Mauro}
                  #and# {Gerevini, Alfonso E.} #and# Hoos,
  title =        {Performance robustness of {AI} planners in the 2014
                  International Planning Competition},
  volume =       31,
  doi =          {10.3233/AIC-170537},
  abstract =     {Solver competitions have been used in many areas of AI to
                  assess the current state of the art and guide future research
                  and development. AI planning is no exception, and the
                  International Planning Competition (IPC) has been frequently
                  run for nearly two decades. Due to the organisational and
                  computational burden involved in running these competitions,
                  solvers are generally compared using a single homogeneous
                  hardware and software environment for all competitors. To
                  what extent does the specific choice of hardware and software
                  environment have an effect on solver performance, and is that
                  effect distributed equally across the competing solvers? In
                  this work, we use the competing planners and benchmark
                  instance sets from the 2014 IPC to investigate these two
                  questions. We recreate the 2014 IPC Optimal and Agile tracks
                  on two distinct hardware environments and eight distinct
                  software environments. We show that solver performance varies
                  significantly based on the hardware and software environment,
                  and that this variation is not equal for all
                  planners. Furthermore, the observed variation is sufficient
                  to change the competition rankings, including the top-ranked
                  planners for some tracks.},
  number =       6,
  journal =      aicomm,
  publisher =    {IOS Press},
  year =         2018,
  month =        dec,
  pages =        {445--463}
}

@Article{BoeKahMud1994,
  author =       {Kenneth D. Boese and Andrew B. Kahng and Sudhakar Muddu},
  title =        {A New Adaptive Multi-Start Technique for Combinatorial Global
                  Optimization},
  journal =      orl,
  year =         1994,
  volume =       16,
  number =       2,
  pages =        {101--113},
  keywords =       "big-valley hypothesis, TSP, landscape analysis"
}

@Article{Boh2009idcs,
  author =       {Marko Bohanec},
  title =        {Decision making: a computer-science and
                  information-technology viewpoint},
  journal =      idcs,
  year =         2009,
  volume =       7,
  number =       2,
  pages =        {22--37},
}

@Article{BohJohSte1986,
  title =        {Generalized Simulated Annealing for Function Optimization},
  author =       Bohachevsky_IO #and# Johnson_ME #and# Stein_ML,
  journal =      techms,
  volume =       28,
  number =       3,
  pages =        {209--217},
  year =         1986,
  publisher =    taylorfrancis
}

@Article{Bor2000,
  title =        "{CHESS} - Changing Horizon Efficient Set Search: A
                  simple principle for multiobjective optimization",
  author =       {Borges, P. C.},
  journal =      joh,
  volume =       6,
  number =       3,
  pages =        {405--418},
  year =         2000,
}

@Article{BorHamTav2007joh,
  author =       "Boros, Endre and Hammer, Peter L.  and Tavares, Gabriel",
  title =        "Local search heuristics for Quadratic Unconstrained Binary
                  Optimization ({QUBO})",
  journal =      joh,
  year =         2007,
  volume =       13,
  number =       2,
  pages =        "99--132"
}

@Article{Borda1781,
  author =       {Jean-Charles de Borda},
  journal =      {Histoire de l'Acad{\'e}mie Royal des Sciences},
  title =        {M{\'e}moire sur les {\'E}lections au Scrutin},
  year =         1781,
  keywords =     "ranking"
}

@Article{BotBon98,
  author =       {Hozefa M. Botee and Eric Bonabeau},
  title =        {Evolving Ant Colony Optimization},
  year =         1998,
  journal =      acs,
  volume =       1,
  pages =        {149--159},
}

@Article{BotSch2019dominance,
  title =        {Dominance for multi-objective robust optimization concepts},
  author =       {Botte, Marco} #and# Schobel_A,
  journal =      ejor,
  volume =       273,
  number =       2,
  pages =        {430--440},
  year =         2019,
  publisher =    {Elsevier}
}

@Article{BouBluBou2012,
  author =       {Salim Bouamama} #and# Blum #and# {Abdellah Boukerram},
  title =        {A Population-based Iterated Greedy Algorithm for the Minimum Weight Vertex Cover Problem},
  journal =      asoco,
  year =         2012,
  volume =       12,
  number =       6,
  pages =        {1632--1639}
}

@Article{BouForGliPir2010:ejor,
  author =       Bous #and# Fortemps #and# Glineur #and# Pirlot,
  title =        "{ACUTA}: {A} novel method for eliciting additive value functions on the basis of holistic preference statements",
  journal =      ejor,
  year =         2010,
  volume =       206,
  number =       2,
  pages =        "435--444",
}

@Article{BouLec2003ejor,
  author =       {Bouleimen, K. and Lecocq, H.},
  title =        {A new efficient simulated annealing algorithm for
                  the resource-constrained project scheduling problem
                  and its multiple mode version},
  volume =       149,
  doi =          {10.1016/S0377-2217(02)00761-0},
  abstract =     {This paper describes new simulated annealing ({SA)}
                  algorithms for the resource-constrained project
                  scheduling problem ({RCPSP)} and its multiple mode
                  version ({MRCPSP).} The objective function
                  considered is minimisation of the makespan. The
                  conventional {SA} search scheme is replaced by a new
                  design that takes into account the specificity of
                  the solution space of project scheduling
                  problems. For {RCPSP}, the search was based on an
                  alternated activity and time incrementing process,
                  and all parameters were set after preliminary
                  statistical experiments done on test instances. For
                  {MRCPSP}, we introduced an original approach using
                  two embedded search loops alternating activity and
                  mode neighbourhood exploration. The performance
                  evaluation done on the benchmark instances available
                  in the literature proved the efficiency of both
                  adaptations that are currently among the most
                  competitive algorithms for these problems.},
  number =       2,
  journal =      ejor,
  year =         2003,
  keywords =     {multi-mode resource-constrained project scheduling,
                  project scheduling, simulated annealing},
  pages =        {268--281},
}

@Article{BozFowGelKim2010or,
  title =        {Quantitative comparison of approximate solution sets for
                  multicriteria optimization problems with weighted
                  {Tchebycheff} preference function},
  author =       {Bozkurt, B. and Fowler, J. W. and Gel, E. S. and Kim, B.}
                  #and# Koksalan #and# Wallenius_J,
  journal =      or,
  year =         2010,
  number =       3,
  pages =        {650--659},
  volume =       58,
  publisher =    {INFORMS},
  annote =       "Proposed IPF indicator"
}

@Article{BraGreSlo2010bpas,
  title =        {Interactive evolutionary multiobjective optimization driven
                  by robust ordinal regression},
  author =       Branke #and# Greco #and# Slowinski #and# {Zielniewicz, P},
  journal =      bpas-ts,
  volume =       58,
  number =       3,
  pages =        {347--358},
  year =         2010,
  doi =          {10.2478/v10175-010-0033-3}
}

@Article{BraGutRAu2006cms,
  author =       {S. C. Brailsford} #and# Gutjahr #and# {M. S. Rauner and
                  W. Zeppelzauer},
  title =        {Combined Discrete-event Simulation and Ant Colony
                  Optimisation Approach for Selecting Optimal Screening
                  Policies for Diabetic Retinopathy},
  journal =      cms,
  year =         2006,
  volume =       4,
  number =       1,
  pages =        {59--83},
  alias =        "Bra++06"
}

@Article{BraKauSch2001aes,
  author =       Branke #and# "Kaussler, T. and Schmeck, H.",
  title =        {Guidance in evolutionary multi-objective optimization},
  journal =      aes,
  year =         2001,
  volume =       32,
  pages =        {499--507}
}

@Article{BraNguPic2016tec,
  author =       Branke #and# {S. Nguyen and C. W. Pickardt} #and# {M. Zhang},
  journal =      tec,
  title =        {Automated Design of Production Scheduling Heuristics: A
                  Review},
  year =         2016,
  volume =       20,
  number =       1,
  pages =        {110--124},
}

@Article{BraSch2005faster,
  title =        {Faster Convergence by Means of Fitness Estimation},
  author =       Branke #and# {Schmidt, C.},
  year =         2005,
  month =        jan,
  journal =      soco,
  volume =       9,
  number =       1,
  pages =        {13--20},
  issn =         {1432-7643, 1433-7479},
  doi =          {10.1007/s00500-003-0329-4},
  langid =       {english}
}

@Article{BraZap2016:cor,
  author =       {Roland Braune and G. Z{\"a}pfel},
  title =        {Shifting Bottleneck Scheduling for Total Weighted Tardiness Minimization---A Computational Evaluation of Subproblem and Re-optimization Heuristics},
  journal =      cor,
  year =         2016,
  volume =       66,
  pages =        {130--140}
}

@Article{BranCorrGreSlow2016ejor,
  author =       Branke #and# Corrente_S #and# Greco #and# Slowinski #and#
                  {Zielniewicz, P.},
  title =        {Using {Choquet} integral as preference model in interactive
                  evolutionary multiobjective optimization},
  journal =      ejor,
  volume =       250,
  number =       3,
  pages =        {884--901},
  year =         2016,
  doi =          {10.1016/j.ejor.2015.10.027}
}

@Article{BranFarSha2016cgti,
  author =       Branke #and# { Farid, S. S.} #and# {Shah, N.},
  title =        {Industry 4.0: a vision for personalized medicine supply
                  chains?},
  journal =      {Cell and Gene Therapy Insights},
  year =         2016,
  volume =       2,
  number =       2,
  pages =        {263--270},
  doi =          "10.18609/cgti.2016.027"
}

@Article{BranGreSlow2015,
  author =       Branke #and# Greco #and# Slowinski #and# {Piotr Zielniewicz},
  title =        {Learning Value Functions in Interactive Evolutionary
                  Multiobjective Optimization},
  journal =      tec,
  year =         2015,
  volume =       19,
  pages =        {88--102},
  number =       1
}

@Article{BranJin2005tec,
  author =       Jin_Yaochu #and# Branke,
  title =        {Evolutionary Optimization in Uncertain Environments---A
                  Survey},
  journal =      tec,
  year =         2005,
  volume =       9,
  number =       5,
  pages =        {303--317},
}

@Article{Breiman2001,
  author=       {Leo Breiman},
  title=        {Random Forests},
  journal=      ml,
  year=         2001,
  volume=       45,
  number=       1,
  pages=        {5--32},
  doi =         {10.1023/A:1010933404324},
}

@Article{BriCabEmm2018maximum,
  title =        {Maximum volume subset selection for anchored boxes},
  author =       Bringmann_K #and# {Cabello, Sergio} #and# Emmerich,
  journal =      arxiv # {1803.00849},
  year =         2018,
  doi =          "10.48550/arXiv.1803.00849",
  abstract =     "Let $B$ be a set of $n$ axis-parallel boxes in $\mathbb{R}^d$
                  such that each box has a corner at the origin and the other
                  corner in the positive quadrant of $\mathbb{R}^d$, and let
                  $k$ be a positive integer.  We study the problem of selecting
                  $k$ boxes in $B$ that maximize the volume of the union of the
                  selected boxes.  This research is motivated by applications
                  in skyline queries for databases and in multicriteria
                  optimization, where the problem is known as the
                  \emph{hypervolume subset selection problem}.  It is known
                  that the problem can be solved in polynomial time in the
                  plane, while the best known running time in any dimension $d
                  \ge 3$ is $\Omega\big(\binom{n}{k}\big)$.  We show that: The
                  problem is NP-hard already in 3 dimensions. In 3 dimensions,
                  we break the bound $\Omega\big(\binom{n}{k}\big)$, by
                  providing an $n^{O(\sqrt{k})}$ algorithm. For any constant
                  dimension $d$, we present an efficient polynomial-time
                  approximation scheme.",
  keywords =     "hypervolume subset selection"
}

@Article{BriFri2012tcs,
  author =       Bringmann_K #and# Friedrich_T,
  title =        {Approximating the Least Hypervolume Contributor: {NP}-Hard in
                  General, But Fast in Practice},
  pages =        {104--116},
  year =         2012,
  volume =       425,
  journal =      tcs,
  doi =          "10.1016/j.tcs.2010.09.026"
}

@Article{BriFri2010eff,
  author =       Bringmann_K #and# Friedrich_T,
  title =        "An efficient algorithm for computing hypervolume
                  contributions",
  journal =      ec,
  volume =       18,
  number =       3,
  pages =        "383--402",
  year =         2010,
}

@Article{BriFri2014convergence,
  title =        {Convergence of hypervolume-based archiving algorithms},
  author =       Bringmann_K #and# Friedrich_T,
  journal =      tec,
  year =         2014,
  number =       5,
  pages =        {643--657},
  volume =       18,
  publisher =    {IEEE},
  keywords =     "competitive ratio",
  doi =          "10.1109/TEVC.2014.2341711",
  annote =       "Proof that all nondecreasing $(\mu + \lambda)$ archiving algorithms with
                  $\lambda < \mu$ are ineffective."
}

@Article{Bro1970bfgs,
  author       = {Broyden, Charles G.},
  title        = {The Convergence of a Class of Double-rank Minimization
                  Algorithms: 2. The New Algorithm},
  journal      = imajam,
  year         = 1970,
  volume       = 6,
  number       = 3,
  pages        = {222--231},
  month        = sep,
  annote       = {One of the four papers that proposed BFGS.},
  doi          = {10.1093/imamat/6.3.222},
  eprint       =
                  {https://academic.oup.com/imamat/article-pdf/6/3/222/1848059/6-3-222.pdf},
  keywords     = {BFGS}
}

@Article{BroBadThiZit2013directed,
  title =        {Directed Multiobjective Optimization Based on the Weighted
                  Hypervolume Indicator},
  volume =       20,
  doi =          {10.1002/mcda.1502},
  abstract =     {Recently, there has been a large interest in set-based
                  evolutionary algorithms for multi objective
                  optimization. They are based on the definition of indicators
                  that characterize the quality of the current population while
                  being compliant with the concept of Pareto-optimality. It has
                  been shown that the hypervolume indicator, which measures the
                  dominated volume in the objective space, enables the design
                  of efficient search algorithms and, at the same time, opens
                  up opportunities to express user preferences in the search by
                  means of weight functions. The present paper contains the
                  necessary theoretical foundations and corresponding
                  algorithms to (i) select appropriate weight functions, to
                  (ii) transform user preferences into weight functions and to
                  (iii) efficiently evaluate the weighted hypervolume indicator
                  through Monte Carlo sampling. The algorithm W-HypE, which
                  implements the previous concepts, is introduced, and the
                  effectiveness of the search, directed towards the user's
                  preferred solutions, is shown using an extensive set of
                  experiments including the necessary statistical performance
                  assessment.},
  number =       {5-6},
  journal =      jmcda,
  author =       Brockhoff #and# Bader_J #and# Thiele #and# Zitzler,
  year =         2013,
  keywords =     {hypervolume, preference-based search, multi objective
                  optimization, evolutionary algorithm},
  pages =        {291--317},
}

@Article{BroCorFre2010tutorial,
  author =       {Brochu, Eric and Cora, Vlad} #and# DeFreitas_N,
  year =         2010,
  month =        dec,
  title =        {A Tutorial on {Bayesian} Optimization of Expensive Cost
                  Functions, with Application to Active User Modeling and
                  Hierarchical Reinforcement Learning},
  journal =      arxiv # {1012.2599},
  url =          {https://arxiv.org/abs/1012.2599}
}

@Article{BroTusTusWag2016biobj,
  author       = Brockhoff #and# Tusar #and# {Dejan Tu{\v s}ar} #and# Wagner_T
                  #and# Hansen_N #and# Auger_A,
  title        = {Biobjective performance assessment with the {COCO} platform},
  journal      = arxiv # {1605.01746},
  year         = 2016,
  doi          = {10.48550/arXiv.1605.01746}
}

@Article{BroWagTrau2015r2,
  title =        {{R2} indicator-based multiobjective search},
  author =       Brockhoff #and# Wagner_T #and# Trautmann,
  journal =      ec,
  year =         2015,
  number =       3,
  pages =        {369--395},
  volume =       23,
}

@Article{BroZit2009ec,
  author =       Brockhoff #and# Zitzler,
  title =        "Objective Reduction in Evolutionary Multiobjective
                  Optimization: Theory and Applications",
  journal =      ec,
  volume =       17,
  number =       2,
  pages =        {135--166},
  year =         2009,
  abstract =     "Many-objective problems represent a major challenge in the
                  field of evolutionary multiobjective optimization, in terms of
                  search efficiency, computational cost, decision making,
                  visualization, and so on. This leads to various research
                  questions, in particular whether certain objectives can be
                  omitted in order to overcome or at least diminish the
                  difficulties that arise when many, that is, more than three,
                  objective functions are involved. This study addresses this
                  question from different perspectives. First, we investigate
                  how adding or omitting objectives affects the problem
                  characteristics and propose a general notion of conflict
                  between objective sets as a theoretical foundation for
                  objective reduction. Second, we present both exact and
                  heuristic algorithms to systematically reduce the number of
                  objectives, while preserving as much as possible of the
                  dominance structure of the underlying optimization
                  problem. Third, we demonstrate the usefulness of the proposed
                  objective reduction method in the context of both decision
                  making and search for a radar waveform application as well as
                  for well-known test functions.",
  doi =          {10.1162/evco.2009.17.2.135},
}

@Article{Broyden1970bfgs,
  author       = {Broyden, C. G.},
  title        = {The Convergence of a Class of Double-rank Minimization
                  Algorithms 1. General Considerations},
  journal      = imajam,
  year         = 1970,
  volume       = 6,
  number       = 1,
  pages        = {76--90},
  month        = mar,
  keywords     = {Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm},
  abstract     = {This paper presents a more detailed analysis of a class of
                  minimization algorithms, which includes as a special case the
                  DFP (Davidon-Fletcher-Powell) method, than has previously
                  appeared. Only quadratic functions are considered but
                  particular attention is paid to the magnitude of successive
                  errors and their dependence upon the initial matrix. On the
                  basis of this a possible explanation of some of the observed
                  characteristics of the class is tentatively suggested.},
  doi          = {10.1093/imamat/6.1.76}
}

@Article{BruHurWer1996,
  author =       {Peter Brucker and Johann Hurink and Frank Werner},
  title =        {Improving Local Search Heuristics for some Scheduling Problems --- {Part} {I}},
  journal =      dam,
  year =         1996,
  volume =       65,
  number =       {1--3},
  pages =        {97--122}
}

@Article{BruHurWer1997,
  author =       {Peter Brucker and Johann Hurink and Frank Werner},
  title =        {Improving Local Search Heuristics for some Scheduling Problems --- {Part} {II}},
  journal =      dam,
  year =         1997,
  volume =       72,
  number =       {1--2},
  pages =        {47--69}
}

@Article{BruJacTho1999:aor,
  author =       {M. J. Brusco and L. W. Jacobs and G. M. Thompson},
  title =        {A Morphing Procedure to Supplement a Simulated
                  Annealing Heuristic for Cost- and
                  Coverage-correlated Set Covering Problems},
  journal =      aor,
  year =         1999,
  volume =       86,
  pages =        {611--627}
}

@Article{Buc1994jors,
  title =        "An experimental evaluation of interactive {MCDM}
                  methods and the decision making process",
  author =       Buchanan,
  journal =      jors,
  pages =        {1050--1059},
  volume =       45,
  number =       9,
  year =         1994,
}

@Article{Buc1997jors,
  author       = Buchanan,
  title        = {A naive approach for solving {MCDM} problems: the {GUESS}
                  method},
  journal      = jors,
  year         = 1997,
  volume       = 48,
  pages        = {202--206}
}

@Article{BucCor1997anchoring,
  title =        {The effects of anchoring in interactive {MCDM} solution
                  methods},
  volume =       24,
  doi =          {10.1016/S0305-0548(97)00014-2},
  number =       10,
  journal =      cor,
  author =       Buchanan #and# {Corner, James L.},
  month =        oct,
  year =         1997,
  pages =        {907--918}
}

@Article{BucGoo2004maxima,
  author =       {A. L. Buchsbaum and M. T. Goodrich},
  title =        {Three-Dimensional Layers of Maxima},
  journal =      algorithmica,
  year =         2004,
  volume =       39,
  pages =        {275--289},
  alias =        {Buchsbaum04},
}

@Article{BulHarStr99:aor,
  author =       Bullnheimer #and# Hartl #and# Strauss,
  title =        "An Improved Ant System Algorithm for the Vehicle Routing
                  Problem",
  journal =      aor,
  year =         1999,
  volume =       89,
  pages =        "319--328",
  alias =        {ASrank99},
}

@Article{BulHarStr99:cejore,
  author =       Bullnheimer #and# Hartl #and# Strauss,
  title =        {A new rank-based version of the {Ant} {System}: {A}
                  computational study},
  journal =      cejore,
  year =         1999,
  volume =       7,
  number =       1,
  pages =        {25--38},
  alias =        {BulHarStr99:ras},
}

@Article{BurByk2017,
  title =        {The Late Acceptance Hill-Climbing Heuristic},
  author =       Burke_E #and# Bykov_Y,
  journal =      ejor,
  volume =       258,
  number =       1,
  pages =        {70--78},
  year =         2017,
}

@Article{BurFin1983,
  title =        {The asymptotic probabilistic behaviour of quadratic sum assignment problems},
  author =       Burkard #and# {Fincke, Ulrich},
  journal =      zor,
  volume =       27,
  number =       1,
  pages =        {73--81},
  year =         1983,
  publisher =    springer
}

@Article{BurFraMos2004:joh,
  author =       {Luciana Buriol} #and# {Paulo M. Fran{\c c}a} #and# Moscato,
  title =        {A New Memetic Algorithm for the Asymmetric Traveling Salesman Problem},
  journal =      joh,
  year =         2004,
  volume =       10,
  number =       5,
  pages =        {483--506}}

@Article{BurGenHyd2013,
  author =       Burke_E #and# Gendreau #and# Hyde_M #and# Kendall_G #and#
                  Ochoa #and# Ozcan_E #and# Qu_R,
  title =        {Hyper-heuristics: A Survey of the State of the Art},
  journal =      jors,
  year =         2013,
  volume =       64,
  number =       12,
  pages =        {1695--1724},
  doi =          "10.1057/jors.2013.71"
}

@Article{BurHydKen2010tec,
  author =       Burke_E #and# Hyde_M #and# Kendall_G #and# Woodward_J,
  journal =      tec,
  title =        {A Genetic Programming Hyper-Heuristic Approach for Evolving
                  {2-D} Strip Packing Heuristics},
  year =         2010,
  volume =       14,
  number =       6,
  pages =        {942--958},
  doi =          {10.1109/TEVC.2010.2041061},
}

@Article{BurHydKen2012ec,
  title =        {Automating the Packing Heuristic Design Process with Genetic
                  Programming},
  author =       Burke_E #and# Hyde_M #and# Kendall_G #and# Woodward_J,
  doi =          {10.1162/evco_a_00044},
  year =         2012,
  volume =       20,
  number =       1,
  pages =        {63--89},
  journal =      ec,
  keywords =     "one-, two-, or three-dimensional knapsack and bin packing
                  problems"
}

@Article{BurHydKen2012tec,
  author =       Burke_E #and# Hyde_M #and# Kendall_G,
  title =        "Grammatical Evolution of Local Search Heuristics",
  journal =      tec,
  volume =       16,
  number =       7,
  year =         2012,
  pages =        {406--417},
  doi =          {10.1109/TEVC.2011.2160401},
}

@Article{BurKarRen1997,
  title =        {{QAPLIB}--a Quadratic Assignment Problem Library},
  author =       Burkard #and# Karisch_SE #and# Rendl_F,
  journal =      jgo,
  volume =       10,
  number =       4,
  pages =        {391--403},
  year =         1997,
  publisher =    springer
}

@Article{BurRen1984,
  author =       Burkard #and# Rendl_F,
  title =        {A Thermodynamically Motivated Simulation Procedure for
                  Combinatorial Optimization Problems},
  journal =      ejor,
  year =         1984,
  volume =       17,
  number =       2,
  pages =        {169--174},
  doi =          {10.1016/0377-2217(84)90231-5},
  keywords =       {2-exchange delta evaluation for QAP}
}

@Article{BusRobTot2014,
  author =       {Erika Buson and Roberto Roberti} #and# Toth_P,
  title =        {A Reduced-Cost Iterated Local Search Heuristic for the Fixed-Charge Transportation Problem},
  journal =      or,
  year =         2014,
  volume =       62,
  number =       5,
  pages =        {1095--1106}
}

@Article{CabLuqMol2002promoin,
  author =       "Caballero, R." #and# Luque_M #and# Molina_J #and# Ruiz_Francisco,
  title =        "{PROMOIN}: An Interactive System for Multiobjective
                  Programming",
  journal =      itdm,
  year =         2002,
  volume =       1,
  pages =        {635--656},
  keywords =     {preferences, multi interactive methods framework}
}

@Article{CacStu2017:endm,
  author =       Perez_L #and# Stuetzle,
  title =        {Exploring Variable Neighborhood Search for Automatic
                  Algorithm Configuration},
  journal =      endm,
  year =         2017,
  volume =       58,
  pages =        {167--174},
  doi =          {10.1016/j.endm.2017.03.022}
}

@Article{CahMelTal2004,
  author =       {Cahon, Sebastien and Melab, Nordine} #and# Talbi,
  title =        "{ParadisEO}: A Framework for the Reusable Design of
                  Parallel and Distributed Metaheuristics",
  doi =          {10.1023/B:HEUR.0000026900.92269.ec},
  journal =      joh,
  number =       3,
  pages =        {357--380},
  volume =       10,
  year =         2004,
  alias =        {CahMelTal04paradiseo},
}

@Article{CaiHuaQuiMa2009,
  author =       {Zhaoquan Cai and Han Huang and Yong Qin and Xianheng
                  Ma},
  title =        {Ant Colony Optimization Based on Adaptive Volatility
                  Rate of Pheromone Trail},
  year =         2009,
  journal =      ijcnss,
  volume =       2,
  number =       8,
  pages =        {792--796},
}

@Article{CaiLiFan2015archive,
  title =        {An external archive guided multiobjective evolutionary
                  algorithm based on decomposition for combinatorial
                  optimization},
  author =       {Cai, Xinye and Li, Yexing and Fan, Zhun} #and# Zhang_Qingfu,
  journal =      tec,
  year =         2015,
  number =       4,
  pages =        {508--523},
  volume =       19,
}

@Article{CaiXiaLiHu2021grid,
  title =        {A grid-based inverted generational distance for
                  multi/many-objective optimization},
  author =       {Cai, Xinye} #and# {Xiao, Yushun} #and# Li_Miqing #and# {Hu,
                  Han} #and# Ishibuchi #and# {Li, Xiaoping},
  journal =      tec,
  year =         2021,
  number =       1,
  pages =        {21--34},
  volume =       25,
  publisher =    {IEEE},
  annote =       "weakly Pareto-compliant indicator"
}

@Article{CaiXiaLiSun2021kernel,
  title =        {A kernel-based indicator for multi/many-objective
                  optimization},
  author =       {Cai, Xinye and Xiao, Yushun and Li, Zhenhua and Sun, Qi and
                  Xu, Hanchuan} #and# Li_Miqing #and# Ishibuchi,
  journal =      tec,
  year =         2021
}

@Article{Calvo00tsptw,
  author =       Wolfler_Calvo_R,
  title =        "A New Heuristic for the Traveling Salesman Problem
                  with Time Windows",
  journal =      ts,
  volume =       34,
  number =       1,
  year =         2000,
  pages =        {113--124},
  doi =          {10.1287/trsc.34.1.113.12284},
  publisher =    informs,
  pdf =          {Calvo00 - A_New_Heuristic_for_TSPTW.pdf}
}

@Article{CamBatAra2020jss,
	title   = "The \rpackage{MOEADr} Package: A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition",
	author  = Campelo #and# Batista #and# Aranha,
	year    = 2020,
	volume  = 92,
	issue   = 6,
	journal = jss,
	doi     = {10.18637/jss.v092.i06}
}

@Article{CamDorStu2019si,
  author =       Camacho_C #and# Dorigo #and# Stuetzle,
  title =        {The intelligent water drops algorithm: why it cannot be considered a novel algorithm},
  journal =      swarm,
  year =         2019,
  volume =       13,
  pages =        {173--192}
}

@Article{CamDorStu2022cuckooSearch,
  title =        {An analysis of why cuckoo search does not bring any novel ideas to optimization},
  author =       Camacho_C  #and# Dorigo  #and# Stuetzle,
  journal =      cor,
  pages =        {105747},
  year =         2022
}

@Article{CamDorStu2022exposing,
  author =       Camacho_C  #and# Dorigo  #and# Stuetzle,
  title =        {Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by bestial metaphors},
  journal =      itor,
  doi =          "10.1111/itor.13176",
  year =         2022,
}

@Article{CamJon2011,
  title =        {Prepositioning supplies in preparation for disasters},
  author =       {Campbell, Ann Melissa and Jones, Philip C.},
  journal =      ejor,
  volume =       209,
  number =       2,
  pages =        {156--165},
  year =         2011,
  publisher =    elsevier
}

@Article{CamSchXia2013sentim,
  author =       "Cambria, E and Schuller, B and Xia, Y and Havasi, C",
  year =         2013,
  title =        "New avenues in opinion mining and sentiment analysis",
  journal =      intsys,
  volume =       28,
  number =       2,
  pages =        "15--21",
  doi =          {10.1109/MIS.2013.30}
}

@Article{CamStuDor2021psox,
  author       = Camacho_C #and# Stuetzle #and# Dorigo,
  title        = {{PSO-X}: A Component-Based Framework for the Automatic Design
                  of Particle Swarm Optimization Algorithms},
  journal      = tec,
  year         = 2021,
  volume       = 26,
  number       = 3,
  pages        = {402--416},
  doi          = {10.1109/TEVC.2021.3102863}
}

@Article{CamWan2020sample,
  author =       Campelo #and# Wanner_E,
  title =        "Sample size calculations for the experimental comparison of
                  multiple algorithms on multiple problem instances",
  journal =      joh,
  year =         2020,
  volume =       26,
  number =       6,
  pages =        {851--883},
  doi =          "10.1007/s10732-020-09454-w",
}

@Article{CaoJiaZhan2017its,
  author =       {Cao, Z. and Jiang, S. and Zhang, J. and Guo, H.},
  title =        "A unified framework for vehicle rerouting and traffic light
                  control to reduce traffic congestion",
  journal =      ieee-tits,
  pages =        "1958--1973",
  volume =       18,
  number =       7,
  year =         2017,
}

@Article{Cap2017,
  title   = {Variable Neighborhood Search for Extremal Vertices : The {AutoGraphiX-III} System},
  author  = {Gilles Caporossi},
  journal = cor,
  volume  = 78,
  pages   = {431--438},
  year    = 2017
}

@Article{Car1982:ejor,
  author = 	 {J. Carlier},
  title = 	 {The One-machine Sequencing Problem},
  journal = 	 ejor,
  year = 	 1982,
  volume =	 11,
  number =       1,
  pages =	 {42--47}
}

@Article{CarBar96tabusearch_tsptw,
  author =       Carlton_William #and# Barnes_JW,
  title =        "Solving the traveling-salesman problem with time
                  windows using tabu search",
  journal =      iiet,
  year =         1996,
  volume =       28,
  pages =        {617--629},
  pdf =          "CarltonBarnes96 -
                  Solving_the_TSPTW_using_Tabu_Search.pdf",
}

@Article{CarKonCor2019inf,
  author =       {Fabio Caraffini} #and# Kononova_A #and# Corne,
  title =        {Infeasibility and structural bias in differential evolution},
  journal =      is,
  volume =       496,
  pages =        {161--179},
  year =         2019,
  doi =          {10.1016/j.ins.2019.05.019},
}

@Article{CasLab99,
  title =        {Heuristics for large constrained vehicle routing problems},
  author =       {Caseau, Yves and Laburthe, Fran{\c{c}}ois},
  journal =      joh,
  volume =       5,
  number =       3,
  pages =        {281--303},
  year =         1999
}

@Article{CasSilLab2001tplp,
  author =       {Yves Caseau and Glenn Silverstein and Fran{\c{c}}ois
                  Laburthe},
  title =        {Learning Hybrid Algorithms for Vehicle Routing
                  Problems},
  journal =      tplp,
  volume =       1,
  number =       6,
  year =         2001,
  pages =        {779--806},
  epub =         {http://arxiv.org/abs/cs/0405092},
}

@Article{CatAbsFeiVig2014:cor,
  author =       {Diego Cattaruzza and Nabil Absi and Dominique Feillet} #and# Vigo_D,
  title =        {An Iterated Local Search for the Multi-commodity Multi-trip Vehicle Routing Problem with Time Windows},
  journal =      cor,
  year =         2014,
  volume =       51,
  pages =        {257--267}
}

@Article{CauNiePok2012,
  title =        {Optimization models in emergency logistics: A literature review},
  author =       {Caunhye, Aakil M. and Nie, Xiaofeng and Pokharel, Shaligram},
  journal =      seps,
  volume =       46,
  number =       1,
  pages =        {4--13},
  year =         2012,
  publisher =    elsevier
}

@Article{CebIruMen2014eda,
  author =       Ceberio #and# Irurozki_E #and# Mendiburu_A #and# Lozano_JA,
  title =        {A distance-based ranking model estimation of distribution
                  algorithm for the flowshop scheduling problem},
  abstract =     {The aim of this paper is two-fold. First, we introduce a
                  novel general estimation of distribution algorithm to deal
                  with permutation-based optimization problems. The algorithm
                  is based on the use of a probabilistic model for permutations
                  called the generalized Mallows model. In order to prove the
                  potential of the proposed algorithm, our second aim is to
                  solve the permutation flowshop scheduling problem. A hybrid
                  approach consisting of the new estimation of distribution
                  algorithm and a variable neighborhood search is
                  proposed. Conducted experiments demonstrate that the proposed
                  algorithm is able to outperform the state-of-the-art
                  approaches. Moreover, from the 220 benchmark instances
                  tested, the proposed hybrid approach obtains new best known
                  results in 152 cases. An in-depth study of the results
                  suggests that the successful performance of the introduced
                  approach is due to the ability of the generalized Mallows
                  estimation of distribution algorithm to discover promising
                  regions in the search space.},
  doi =          {10.1109/TEVC.2013.2260548},
  journal =      tec,
  keywords =     {Estimation of distribution algorithms,Generalized Mallows
                  model,Permutation flowshop scheduling
                  problem,Permutations-based optimization problems},
  number =       2,
  pages =        {286--300},
  volume =       18,
  year =         2014
}

@Article{Cer85,
  author =       "Vladim{\'i}r \v{C}ern\'y",
  title =        "A Thermodynamical Approach to the Traveling Salesman Problem: An Efficient Simulation Algorithm",
  journal =      jota,
  year =         1985,
  volume =       45,
  number =       1,
  pages =        "41--51",
}

@Article{CerVas2010mga,
  author       = {Ceriotti, Matteo and Vasile, Massimiliano},
  title        = {Automated Multigravity Assist Trajectory Planning with a
                  Modified Ant Colony Algorithm},
  journal      = {Journal of Aerospace Computing, Information, and
                  Communication},
  year         = 2010,
  volume       = 7,
  number       = 9,
  pages        = {261--293},
  doi          = {10.2514/1.48448}
}

@Article{CesDiGSch2012,
  title =        {Design, Engineering, and Experimental Analysis of a Simulated Annealing Approach to the Post-Enrolment Course Timetabling Problem},
  author =       Ceschia_S #and# DiGaspero #and# Schaerf,
  journal =      cor,
  volume =       39,
  number =       7,
  pages =        {1615--1624},
  year =         2012,
  publisher =    elsevier
}

@Article{CesSch2012aim,
  author =       Ceschia_S #and# Schaerf,
  title =        {Modeling and solving the dynamic patient admission scheduling problem under uncertainty},
  journal =      aiim,
  volume =       56,
  number =       3,
  pages =        {199--205},
  year =         2012 ,
  doi =          {10.1016/j.artmed.2012.09.001},
  keywords =     {F-race},
}

@Article{CesSchStu2013:cie,
  author =       Ceschia_S #and# Schaerf #and# Stuetzle,
  title =        {Local Search Techniques for a Routing-packing Problem},
  journal =      cie,
  year =         2013,
  volume =       66,
  number =       4,
  pages =        {1138--1149},
}

@Article{ChaMeaBea2000cor,
  author       = {T.-J. Chang and N. Meade} #and# Beasley #and#
                  {Y. M. Sharaiha},
  title        = {Heuristics for cardinality constrained portfolio
                  optimisation},
  journal      = cor,
  year         = 2000,
  volume       = 27,
  number       = 13,
  pages        = {1271--1302},
  keywords     = {Portfolio optimisation, CCMVPOP, Efficient frontier},
  abstract     = {In this paper we consider the problem of finding the
                  efficient frontier associated with the standard mean-variance
                  portfolio optimisation model. We extend the standard model to
                  include cardinality constraints that limit a portfolio to
                  have a specified number of assets, and to impose limits on
                  the proportion of the portfolio held in a given asset (if any
                  of the asset is held). We illustrate the differences that
                  arise in the shape of this efficient frontier when such
                  constraints are present. We present three heuristic
                  algorithms based upon genetic algorithms, tabu search and
                  simulated annealing for finding the cardinality constrained
                  efficient frontier. Computational results are presented for
                  five data sets involving up to 225 assets.  Scope and purpose
                  The standard Markowitz mean-variance approach to portfolio
                  selection involves tracing out an efficient frontier, a
                  continuous curve illustrating the tradeoff between return and
                  risk (variance). This frontier can be easily found via
                  quadratic programming. This approach is well-known and widely
                  applied. However, for practical purposes, it may be desirable
                  to limit the number of assets in a portfolio, as well as
                  imposing limits on the proportion of the portfolio devoted to
                  any particular asset. If such constraints exist, the problem
                  of finding the efficient frontier becomes much harder. This
                  paper illustrates how, in the presence of such constraints,
                  the efficient frontier becomes discontinuous. Three heuristic
                  techniques are applied to the problem of finding this
                  efficient frontier and computational results presented for a
                  number of data sets which are made publicly available.}
}

@Article{ChaWag2015many,
  author =       "Shelvin Chand" #and# Wagner_M,
  title =        "Evolutionary many-objective optimization: A quick-start
                  guide",
  journal =      sorms,
  volume =       20,
  number =       2,
  pages =        "35--42",
  year =         2015,
  doi =          "10.1016/j.sorms.2015.08.001",
}

@Article{Chase93,
  author =       Chase #and# Ormsbee,
  title =        {Computer-generated pumping schedules for satisfying
                  operation objectives},
  journal =      jawwa,
  year =         1993,
  volume =       85,
  number =       7,
  pages =        {54--61},
}

@Article{ChauDeb2010asoft,
  title =        {An interactive evolutionary multi-objective optimization and
                  decision making procedure},
  author =       {Chaudhuri, Shamik} #and# Deb,
  journal =      asoco,
  volume =       10,
  number =       2,
  pages =        {496--511},
  year =         2010,

}

@Article{CheChiSto2012,
  title =        {Business Intelligence and Analytics: From Big Data to Big
                  Impact},
  author =       {Chen, Hsinchun and Chiang, Roger H. L. and Storey, Veda C.},
  journal =      misquart,
  volume =       36,
  number =       4,
  pages =        {1165--1188},
  year =         2012
}

@Article{CheHaoGlo2016,
  title =        {A hybrid metaheuristic approach for the capacitated arc
                  routing problem},
  author =       {Yuning Chen} #and# Hao_JK #and# Glover_F,
  journal =      ejor,
  volume =       553,
  number =       1,
  pages =        {25--39},
  year =         2016,
  doi =          {10.1016/j.ejor.2016.02.015},
  keywords =     {irace},
}

@Article{CheHsi2014,
  author =       {Chen, Ruey-Maw and Hsieh, Fu-Ren},
  title =        {An exchange local search heuristic based scheme for
                  permutation flow shop problems},
  journal =      amis,
  volume =       8,
  number =       1,
  pages =        {209--215},
  year =         2014
}

@Article{CheLi1999gen,
  author =       { F. Y.  Cheng and X. S.  Li },
  title =        {Generalized center method for multiobjective engineering
                  optimization},
  journal =      eo,
  volume =       31,
  number =       5,
  pages =        {641--661},
  year =         1999,
  doi =          {10.1080/03052159908941390},
}

@Article{CheLiYao2017dynamic,
  author       = {Chen, Renzhi} #and# {Li, Ke} #and# Yao_Xin,
  title        = {Dynamic Multiobjectives Optimization With a Changing Number
                  of Objectives},
  journal      = tec,
  year         = 2017,
  volume       = 22,
  number       = 1,
  pages        = {157--171},
  annote       = {two co-evolving populations (two archive)},
  doi          = {10.1109/TEVC.2017.2669638}
}

@Article{CheSia2000ejor,
  title   = {Tabu search applied to global optimization},
  author  = {Rachid Chelouah and Patrick Siarry},
  journal = ejor,
  volume  = 123,
  number  = 2,
  pages   = {256--270},
  year    = 2000
}

@Article{ChenChenGon2014tcyb,
  title =        {An evolutionary algorithm with double-level archives for
                  multiobjective optimization},
  author =       {Chen, Ni and Chen, Wei-Neng and Gong, Yue-Jiao and Zhan,
                  Zhi-Hui and Zhang, Jun and Li, Yun and Tan, Yu-Song},
  journal =      tcyb,
  year =         2014,
  number =       9,
  pages =        {1851--1863},
  volume =       45,
  publisher =    {IEEE}
}

@Article{ChengMao07:acs_tsptw,
  author =       {Chin-Bin Cheng and Chun-Pin Mao},
  title =        {A modified ant colony system for solving the
                  travelling salesman problem with time windows},
  journal =      mcm,
  year =         2007,
  volume =       46,
  pages =        {1225--1235},
  doi =          {10.1016/j.mcm.2006.11.035},
  pdf =          {ChinChun07-acs-tsptw.pdf}
}

@Article{ChiBirSocRos2006jos,
  author =       Chiarandini #and# Birattari #and# Socha #and#
                  Rossi-Doria,
  title =        {An Effective Hybrid Algorithm for University Course
                  Timetabling},
  journal =      jos,
  year =         2006,
  volume =       9,
  pages =        {403--432},
  number =       5,
  month =        oct,
  doi =          {10.1007/s10951-006-8495-8},
  keywords =     {2003 international timetabling competition, F-race}
}

@Article{ChiCorDamBau11,
  author =       {Chica, Manuel} #and# Cordon #and# {Damas,
                  Sergio and Bautista, Joaqu{\'i}n},
  year =         2011,
  issn =         {1865-9284},
  journal =      mc,
  volume =       3,
  number =       1,
  title =        {A New Diversity Induction Mechanism for a
                  Multi-objective Ant Colony Algorithm to Solve a
                  Real-world time and Space Assembly Line Balancing
                  Problem},
  pages =        {15--24}
}

@Article{ChiUlySha2016,
  author =       "D. S. Chivilikhin and V. I. Ulyantsev and A. A. Shalyto",
  title =        {Modified ant colony algorithm for constructing finite state machines from execution scenarios and temporal formulas},
  journal =      arc,
  year =         2016,
  volume =       77,
  number =       3,
  pages =        {473--484},
  doi =          {10.1134/S0005117916030097},
  keywords =     {irace},
}

@Article{Chicano2011ecj,
  author =       Chicano_F #and# Whitley #and# Alba_E,
  title =        {A Methodology to Find the Elementary Landscape Decomposition
                  of Combinatorial Optimization Problems},
  journal =      ec,
  year =         2011,
  volume =       19,
  pages =        {597--637},
  number =       4,
}

@Article{Chicano2012aml,
  author =       Chicano_F #and# Luque_G #and# Alba_E,
  title =        {Autocorrelation Measures for the Quadratic Assignment
                  Problem},
  journal =      aml,
  year =         2012,
  volume =       25,
  pages =        {698--705},
  doi =          {10.1016/j.aml.2011.09.053},
}

@Article{ChrMinTot81tsptw,
  author =       Christofides #and# {A. Mingozzi} #and# Toth_P,
  title =        {State-space relaxation procedures for the
                  computation of bounds to routing problems},
  volume =       11,
  number =       2,
  journal =      networks,
  year =         1981,
  pages =        {145--164},
  doi =          {10.1002/net.3230110207},
  anote =        {makespan optimization},
}

@Article{ChuJinMie2018surrogate,
  author =       Chugh_Tinkle #and# Jin_Yaochu #and# Miettinen #and# {Hakanen,
                  Jussi and Sindhya, Karthik},
  journal =      tec,
  title =        {A Surrogate-Assisted Reference Vector Guided Evolutionary
                  Algorithm for Computationally Expensive Many-Objective
                  Optimization},
  year =         2018,
  volume =       22,
  number =       1,
  pages =        {129--142},
  month =        feb,
}

@Article{ChuSinHak2019surv,
  author =       Chugh_Tinkle #and# {Sindhya, Karthik} #and# "Hakanen, Jussi"
                  #and# Miettinen,
  title =        "A survey on handling computationally expensive multiobjective
                  optimization problems with evolutionary algorithms",
  journal =      soco,
  pages =        {3137--3166},
  volume =       23,
  number =       9,
  year =         2019,
  abstract =     "Evolutionary algorithms are widely used for solving
                  multiobjective optimization problems but are often criticized
                  because of a large number of function evaluations
                  needed. Approximations, especially function approximations,
                  also referred to as surrogates or metamodels are commonly
                  used in the literature to reduce the computation time. This
                  paper presents a survey of 45 different recent algorithms
                  proposed in the literature between 2008 and 2016 to handle
                  computationally expensive multiobjective optimization
                  problems. Several algorithms are discussed based on what kind
                  of an approximation such as problem, function or fitness
                  approximation they use. Most emphasis is given to function
                  approximation-based algorithms. We also compare these
                  algorithms based on different criteria such as metamodeling
                  technique and evolutionary algorithm used, type and
                  dimensions of the problem solved, handling constraints,
                  training time and the type of evolution control. Furthermore,
                  we identify and discuss some promising elements and major
                  issues among algorithms in the literature related to using an
                  approximation and numerical settings used. In addition, we
                  discuss selecting an algorithm to solve a given
                  computationally expensive multiobjective optimization problem
                  based on the dimensions in both objective and decision spaces
                  and the computation budget available.",
  doi =          "10.1007/s00500-017-2965-0",
}

@Article{CinFerLopAlb2022irace,
  author       = Cintrano #and# Ferrer_J #and# Lopez-Ibanez #and# Alba_E,
  title        = {Hybridization of Evolutionary Operators with Elitist Iterated
                  Racing for the Simulation Optimization of Traffic Lights
                  Programs},
  journal      = ec,
  year         = 2023,
  volume       = 31,
  number       = 1,
  pages        = {31--51},
  doi          = {10.1162/evco_a_00314},
  abstract     = {In the traffic light scheduling problem the evaluation of
                  candidate solutions requires the simulation of a process
                  under various (traffic) scenarios. Thus, good solutions
                  should not only achieve good objective function values, but
                  they must be robust (low variance) across all different
                  scenarios. Previous work has shown that combining IRACE with
                  evolutionary operators is effective for this task due to the
                  power of evolutionary operators in numerical optimization. In
                  this paper, we further explore the hybridization of
                  evolutionary operators and the elitist iterated racing of
                  IRACE for the simulation-optimization of traffic light
                  programs. We review previous works from the literature to
                  find the evolutionary operators performing the best when
                  facing this problem to propose new hybrid algorithms. We
                  evaluate our approach over a realistic case study derived
                  from the traffic network of Málaga (Spain) with 275 traffic
                  lights that should be scheduled optimally. The experimental
                  analysis reveals that the hybrid algorithm comprising IRACE
                  plus differential evolution offers statistically better
                  results than the other algorithms when the budget of
                  simulations is low. In contrast, IRACE performs better than
                  the hybrids for high simulations budget, although the
                  optimization time is much longer.},
  keywords     = {irace, Simulation optimization, Uncertainty, Traffic light
                  planning}
}

@Article{CirHoe2013mdd,
  author       = Cire_A #and# {van Hoeve, Willem-Jan},
  title        = {Multivalued Decision Diagrams for Sequencing Problems},
  journal      = or,
  year         = 2013,
  volume       = 61,
  number       = 6,
  pages        = {1259--1462},
  doi          = {10.1287/opre.2013.1221}
}

@Article{Clark95,
  author =       Clark #and# Rossman #and# Wymer,
  title =        {Modeling distribution system water quality:
                  regulatory implications},
  journal =      jwrpm,
  year =         1995,
  volume =       121,
  number =       6,
  pages =        {423--428},
  note =         notreadyet,
}

@Article{CleKen2022tec,
  author =       Clerc_M #and# Kennedy_J,
  title =        "The particle swarm - explosion, stability, and convergence in
                  a multidimensional complex space",
  journal =      tec,
  volume =       6,
  number =       1,
  pages =        {58--73},
  month =        feb,
  year =         2002,
  doi =          "10.1109/4235.985692"
}

@Article{CocDraBes2020threats,
  author =       {Cockburn, Andy and Dragicevic, Pierre and Besan\c{c}on, Lonni
                  and Gutwin, Carl},
  title =        {Threats of a Replication Crisis in Empirical Computer
                  Science},
  year =         2020,
  volume =       63,
  number =       8,
  doi =          {10.1145/3360311},
  abstract =     {Research replication only works if there is confidence built
                  into the results.},
  journal =      cacm,
  month =        jul,
  pages =        {70--79},
  numpages =     10
}

@Article{CodManMarRes96:informs,
  author =       {B. Codenotti and G. Manzini and L. Margara and G. Resta},
  title =        {Perturbation: An Efficient Technique for the
                  Solution of Very Large Instances of the
                  {Euclidean} {TSP}},
  journal =      informsjoc,
  year =         1996,
  volume =       8,
  number =       2,
  pages =        {125--133}
}

@Article{Coe2002constraint,
  author =       Coello,
  title =        {Theoretical and numerical constraint-handling techniques used
                  with evolutionary algorithms: a survey of the state of the
                  art},
  journal =      CMinAppMechEng,
  year =         2002,
  volume =       191,
  number =       {11-12},
  pages =        {1245--1287},
  doi =          "10.1016/S0045-7825(01)00323-1"
}

@Article{Coello2003tec-emo,
  title =        {Special Issue on {Evolutionary} {Multiobjective}
                  {Optimization}},
  author =       Coello,
  journal =      tec,
  year =         2003,
  volume =       7,
  number =       2,
  alias =        "Coello03"
}

@Article{Coello2006evolutionary,
  title =        {Evolutionary multi-objective optimization: a historical view
                  of the field},
  author =       Coello,
  journal =      cim,
  volume =       1,
  number =       1,
  pages =        {28--36},
  year =         2006,
}

@Article{CohFie1999:siamo,
  author =   Cohn_H #and# Fielding_M,
  title =    "Simulated Annealing: Searching for an Optimal Temperature",
  journal =  siamo,
  year =     1999,
  volume =   9,
  number =   3,
  pages =    {779--802}
}

@Article{ColBry05:jwrpm,
  author =       Colombo_Andrew #and# Karney_Bryan,
  title =        "Impacts of Leaks on Energy Consumption in Pumped
                  Systems with Storage",
  journal =      jwrpm,
  year =         2005,
  volume =       131,
  number =       2,
  pages =        {146--155},
  month =        mar,
}

@Article{ColDorManTru1994:jorbel,
  author =       Colorni #and# Dorigo #and# Maniezzo #and# {M. Trubian},
  title =        "{Ant} {System} for Job-shop Scheduling",
  journal =      jorbel,
  year =         1994,
  volume =       34,
  number =       1,
  pages =        "39--53",
  alias =        "ColDorManTru94:jorbel"
}

@Article{ColSchPae2010setting,
  title =        "Setting the Research Agenda in Automated Timetabling: The
                  Second International Timetabling Competition",
  author =       McCollum #and# Schaerf #and# Paechter #and# McMullan #and#
                  Lewis_R #and# Parkes_AJ #and# DiGaspero #and# Qu_R #and#
                  Burke_E,
  doi =          {10.1287/ijoc.1090.0320},
  year =         2010,
  month =        feb,
  volume =       22,
  number =       1,
  pages =        {120--130},
  journal =      informs,
}

@Article{ConPotVel02,
  author =       {Richard K. Congram} #and# Potts #and#  {Steve van de Velde},
  title =        {An Iterated Dynasearch Algorithm for the
                  Single-Machine Total Weighted Tardiness Scheduling
                  Problem},
  journal =      informsjoc,
  year =         2002,
  volume =       14,
  number =       1,
  pages =        {52--67}
}

@Article{Connolly1990,
  author =       Connolly_DT,
  title =        {An Improved Annealing Scheme for the {QAP}},
  journal =      ejor,
  volume =       46,
  number =       1,
  pages =        {93--100},
  year =         1990,
  publisher =    elsevier
}

@Article{CooFar1996mul,
  author =       {Cook, Richard J. and Farewell, Vern T.},
  year =         1996,
  title =        {Multiplicity Considerations in the Design and Analysis of
                  Clinical Trials},
  journal =      rssa,
  volume =       159,
  pages =        {93--110},
  annote =       {multiplicity; multiple endpoints; multiple treatments;
                  p-value adjustment; type I error; argues that if results are
                  intended to be interpreted marginally, there may be no need
                  for controlling experimentwise error rate}
}

@Article{CopFleRur2010ordering,
  title =        {Ordering by Weighted Number of Wins Gives a Good Ranking for
                  Weighted Tournaments},
  author =       {Coppersmith, Don and Fleischer, Lisa K. and Rurda, Atri},
  doi =          {10.1145/1798596.1798608},
  journal =      acm-talgo,
  keywords =     {Approximation algorithms,Borda's method,feedback arc set
                  problem,rank aggregation,tournaments},
  month =        jul,
  number =       3,
  volume =       6,
  pages =        {1--13},
  publisher =    {ACM},
  year =         2010
}

@Article{CopGilSch2024decision,
  author       = {Copp{\'e}, Vianney and Gillard, Xavier and Schaus, Pierre},
  title        = {Decision Diagram-Based Branch-and-Bound with Caching for
                  Dominance and Suboptimality Detection},
  journal      = informsjoc,
  year         = 2024,
  doi          = {10.1287/ijoc.2022.0340},
}

@Article{CorBuc1997ejor,
  author       = {Corner, James L.} #and# Buchanan,
  title        = {Capturing decision maker preference: Experimental comparison
                  of decision analysis and {MCDM} techniques},
  journal      = ejor,
  year         = 1997,
  volume       = 98,
  number       = 1,
  pages        = {85--97}
}

@Article{CorDam2006,
  author =       Cordon #and# {Sergio Damas},
  title =        {Image Registration with Iterated Local Search},
  journal =      joh,
  year =         2006,
  volume =       12,
  number =       {1--2},
  pages =        {73--94}
}

@Article{CorDanDep2019,
  author =       Corstjens_J #and# Dang_Nguyen #and# Depaire_B #and# Caris_A #and# DeCausmaecker,
  title =        "A combined approach for analysing heuristic algorithms",
  journal =      joh,
  year =         2019,
  volume=        25,
  number =       4,
  pages =        {591--628},
  doi =          {10.1007/s10732-018-9388-7}
}

@Article{CorDepCarSor2020,
  title =        {A multilevel evaluation method for heuristics with an application to the {VRPTW}},
  author=        Corstjens_J #and# Depaire_B #and# Caris_A #and# Sorensen,
  journal =      itor,
  year =         2020,
  volume =       27,
  number =       1,
  pages =        {168--196},
  doi =          {10.1111/itor.12631}
}

@Article{CorKoz04:compu,
  author =       {P. Corry and E. Kozan},
  title =        {Ant Colony Optimisation for Machine Layout Problems},
  journal =      coa,
  year =         2004,
  volume =       28,
  number =       3,
  pages =        {287--310}
}

@Article{CorLapMer2001,
  author  = {Jean{-}Fran{\c{c}}ois Cordeau} #and# Laporte #and# {A. Mercier},
  title   = "A unified tabu search heuristic for vehicle routing problems with time windows",
  journal = jors,
  year    = 2001,
  volume  = 52,
  number  = 8,
  pages   = {928--936}
}

@Article{CorMai2012:cor,
  author =       {Jean{-}Fran{\c{c}}ois Cordeau and Mirko Maischberger},
  title =        {A Parallel Iterated Tabu Search Heuristic for Vehicle Routing Problems},
  journal =      cor,
  year =         2012,
  volume =       39,
  number =       9,
  pages =        {2033--2050}
}

@Article{CosGolGol2012:esa,
  title =        "Hybridizing {VNS} and path-relinking on a particle swarm
                  framework to minimize total flowtime ",
  journal =      eswa,
  volume =       39,
  number =       18,
  pages =        "13118--13126",
  year =         2012,
  author =       "Wagner Emanoel Costa" #and# Goldbarg_MC #and# Goldbarg_EFG,
}

@Article{CosHer97:jors,
  author =   {D. Costa and A. Hertz},
  title =    {Ants can color graphs},
  journal =  jors,
  year =     1997,
  volume =   48,
  pages =    {295--305},
  alias = "CosHer97"
}

@Article{CoyGolRunWas2001,
  author = {S. P. Coy and B. L. Golden and G. C. Runger and E. A. Wasil},
  title =  {Using Experimental Design to Find Effective
             Parameter Settings for Heuristics},
  journal = joh,
  year = 2001,
  volume = 7,
  number = 1,
  pages = {77--97},
}

@Article{Crabtree1995,
  title =        {Resource Scheduling: Comparing Simulated Annealing with Constraint Programming},
  author =       Crabtree_IB,
  journal =      {BT Technology Journal},
  volume =       13,
  number =       1,
  pages =        {121--127},
  year =         1995,
  publisher =    springer
}

@Article{CriFliVer1996rankings,
  author =       {Critchlow, Douglas Edward and Fligner, Michael A. and
                  Verducci, Joseph S.},
  journal =      jmatpsy,
  pages =        {294--318},
  title =        {Probability Models on Rankings},
  volume =       35,
  year =         1991
}

@Article{Cro1958,
  author =       {G. A. Croes},
  title =        {A Method for Solving Traveling Salesman Problems},
  journal =      or,
  year =         1958,
  volume =       6,
  pages =        {791--812}
}

@Article{CroDemMul1978reporting,
  author =       {Harlan P. Crowder and Ron S. Dembo and John M. Mulvey},
  title =        {Reporting computational experiments in mathematical
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  journal =      mp,
  volume =       15,
  number =       1,
  pages =        {316--329},
  year =         1978,
  doi =          {10.1007/BF01609036},
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}

@Article{CruGonPel2011,
  author = 	 {Carlos Cruz and Juan Ram{\'{o}}n Gonz{\'{a}}lez and David A. Pelta},
  title = 	 {Optimization in Dynamic Environments: A Survey on Problems, Methods
               and Measures},
  journal = 	 soco,
  year = 	 2011,
  volume = 	 15,
  number = 	 7,
  pages = 	 {1427--1448}
}

@Article{CruSubBruOir2017:cor,
  author = 	 {F{\'a}bio Cruz} #and# Subramanian #and# {Bruno P. Bruck} #and# Iori,
  title = 	 {A Heuristic Algorithm for a Single Vehicle Static Bike Sharing Rebalancing Problem},
  journal = 	 cor,
  volume = 	 79,
  year = 	 2017,
  pages = 	 {19--33}
}

@Article{Cul1998ec,
  author =       Culberson_JC,
  title =        {On the Futility of Blind Search: An Algorithmic View of ``No
                  Free Lunch''},
  journal =      ec,
  year =         1998,
  volume =       6,
  number =       2,
  pages =        {109--127},
  doi =          {10.1162/evco.1998.6.2.109},
  keywords =     {NFL}
}

@Article{CzyJas1998,
  title =        "{Pareto} simulated annealing -- a metaheuristic
                  technique for multiple-objective combinatorial
                  optimization",
  author =       {Czyz{\.z}ak, P. } #and# Jaszkiewicz,
  journal =      jmcda,
  volume =       7,
  number =       1,
  pages =        {34--47},
  year =         1998
}

@Article{DamHickRago2010,
  title =        {On Energy, Discrepancy and Group Invariant Measures on
                  Measurable Subsets of {Euclidean} Space},
  author =       {Damelin, Steven B. and Hickernell, Fred J. and Ragozin, David
                  L. and Zeng, Xiaoyan},
  journal =      jfaa,
  year =         2010,
  number =       6,
  pages =        {813--839},
  volume =       16,
  keywords =     {Capacity; Cubature; Discrepancy; Distribution; Group
                  invariant kernel; Group invariant measure; Energy minimizer;
                  Equilibrium measure; Numerical integration; Positive
                  definite; Potential field; Riesz kernel; Reproducing Hilbert
                  space; Signed measure},
  publisher =    {SP Birkhäuser Verlag Boston},
  alias =        "Damelin2010"
}

@Article{Damas:2001:PDW,
  author =       "M. Damas and M. Salmer{\'o}n and J. Ortega and
                  G. Olivares and H. Pomares",
  title =        "Parallel Dynamic Water Supply Scheduling in a
                  Cluster of Computers",
  journal =      j-CCPE,
  volume =       13,
  number =       15,
  pages =        "1281--1302",
  day =          25,
  month =        dec,
  year =         2001,
  coden =        "CCPEBO",
  issn =         "1532-0626 (print), 1532-0634 (electronic)",
}

@Article{DanPoz2020,
  author       = {Augusto Dantas and Aurora Pozo},
  title        = {On the use of fitness landscape features in meta-learning
                  based algorithm selection for the quadratic assignment
                  problem},
  journal      = tcs,
  year         = 2020,
  volume       = 805,
  pages        = {62--75},
  doi          = {10.1016/j.tcs.2019.10.033}
}

@Article{DanRotLep2005,
  title =        {Exploring relaxation induced neighborhoods to improve {MIP} solutions},
  author =       {Danna, Emilie and Rothberg, Edward and Le Pape, Claude},
  journal=       mp,
  volume =       102,
  number =       1,
  pages =        {71--90},
  year =         2005,
  publisher =    springer
}

@Article{DanWol1960,
  title     = {Decomposition Principle for Linear Programs},
  author    = {George B. Dantzig and Philip Wolfe},
  journal   = or,
  volume    = 8,
  number    = 1,
  pages     = {101--111},
  year      = 1960,
}

@Article{DaoLieVerAgu2017ec,
  author =       {Fabio Daolio} #and# Liefooghe #and# Verel #and# Aguirre #and#
                  Tanaka,
  journal =      ec,
  number =       4,
  pages =        {555--585},
  title =        {Problem Features versus Algorithm Performance on Rugged
                  Multiobjective Combinatorial Fitness Landscapes},
  volume =       25,
  year =         2017,
  doi =          {10.1162/evco_a_00193}
}

@Article{DasDen1997,
  author =       {Das, Indraneel and Dennis, John E.},
  title =        {A closer look at drawbacks of minimizing weighted
                  sums of objectives for {Pareto} set generation in
                  multicriteria optimization problems},
  journal =      structopt,
  year =         1997,
  volume =       14,
  number =       1,
  pages =        {63--69},
  doi =          {10.1007/BF01197559}
}

@Article{DasDen1998normal,
  author       = {Das, Indraneel and Dennis, John E.},
  title        = {Normal-boundary intersection: A new method for generating the
                  {Pareto} surface in nonlinear multicriteria optimization
                  problems},
  journal      = siamo,
  year         = 1998,
  volume       = 8,
  number       = 3,
  pages        = {631--657},
  keywords     = {simplex lattice design}
}

@Article{DasMulSug2016de,
  title =        {Recent advances in differential evolution--{An} updated
                  survey},
  author =       Das_S #and# {Mullick, Sankha Subhra} #and# Suganthan,
  journal =      swevo,
  volume =       27,
  pages =        {1--30},
  year =         2016,
}

@Article{DasSug2011:tec,
  author = Das_S #and# Suganthan,
  title = {Differential Evolution: A Survey of the State-of-the-art},
  journal = tec,
  year = 2011,
  volume = 15,
  number = 1,
  month = feb
}

@Article{Dash05,
  author =       {Sanjeeb Dash},
  title =        {Exponential Lower Bounds on the Lengths of Some Classes
               of Branch-and-Cut Proofs},
  journal =      mor,
  year =         2005,
  volume =       30,
  number =       3,
  pages =        {678--700}
}

@Article{DasDiaYan2016chord,
  title =        {How good is the {Chord} algorithm?},
  author =       {Daskalakis, Constantinos and Diakonikolas, Ilias} #and#
                  Yannakakis,
  journal =      siamc,
  year =         2016,
  number =       3,
  pages =        {811--858},
  volume =       45,
}

@Article{DauDouPesKie2010:po2,
  author =       Daunizeau #and# denOuden #and# Pessiglione #and# Kiebel #and# Friston #and# Stephan,
  title =        "Observing the observer ({II}): deciding when to decide",
  journal =      pone,
  year =         2010,
  volume =       5,
  number =       12,
  pages =        "e15555",
  doi =          "10.1371/journal.pone.0015555",
}

@Article{DauDouPesSte2010:po1,
  author =       Daunizeau #and# denOuden #and# Pessiglione #and# Stephan #and# Kiebel #and# Friston,
  title =        "Observing the observer ({I}): meta-{Bayesian} models of learning and decision-making",
  journal =      pone,
  year =         2010,
  volume =       5,
  number =       12,
  pages =        "e15554",
  doi =          "10.1371/journal.pone.0015554",
}

@Article{Deb00constraint,
  author =       Deb,
  title =        {An efficient constraint handling method for genetic
                  algorithms},
  journal =      CMinAppMechEng,
  year =         2000,
  volume =       186,
  number =       {2/4},
  pages =        {311--338},
  doi =          {10.1016/S0045-7825(99)00389-8}
}

@Article{Deb02nsga2,
  author =       Deb #and# Pratap #and# Agarwal #and# Meyarivan,
  title =        {A fast and elitist multi-objective genetic
                  algorithm: {NSGA-II}},
  journal =      tec,
  year =         2002,
  volume =       6,
  number =       2,
  pages =        {182--197},
  doi =          {10.1109/4235.996017},
}

@Article{Deb1999ec,
  author =       Deb,
  title =        "Multi-objective genetic algorithms: problem
                  difficulties and construction of test problems",
  journal =      ec,
  year =         1999,
  volume =       7,
  number =       3,
  pages =        {205--230},
  annote =       {Naive definition of PLO-set}
}

@Article{DebAgr1995sbx,
  author =       Deb #and# Agrawal_RB,
  title =        {Simulated binary crossover for continuous search
                  spaces},
  journal =      cs,
  volume =       9,
  number =       2,
  pages =        {115--148},
  year =         1995,
  epub =
                  {http://www.complex-systems.com/abstracts/v09_i02_a02.html},
  keywords =     {SBX},
}

@Article{DebDeb2014,
  author       = Deb #and# {Debayan Deb},
  title        = {Analysing mutation schemes for real-parameter genetic
                  algorithms},
  journal      = ijaisc,
  year         = 2014,
  volume       = 4,
  number       = 1,
  pages        = {1--28},
  annote       = {Proposed Gaussian mutation}
}

@Article{DebGupDauBran2009reliab,
  author =       Deb #and# {S. Gupta} #and# {D. Daum} #and# Branke #and#
                  {A. Mall} #and# {D. Padmanabhan},
  title =        "Reliability-based optimization using evolutionary algorithms",
  journal =      tec,
  volume =       13,
  number =       5,
  pages =        "1054--1074",
  month =        oct,
  year =         2009,
  doi =          "10.1109/TEVC.2009.2014361"
}

@Article{DebJain2014:nsga3-part1,
  author =       Deb #and# Jain_H,
  journal =      tec,
  title =        {An Evolutionary Many-Objective Optimization Algorithm Using
                  Reference-Point-Based Nondominated Sorting Approach, Part
                  {I}: Solving Problems With Box Constraints},
  year =         2014,
  volume =       18,
  number =       4,
  pages =        {577--601},
  annote =     "Proposed NSGA-III"
}

@Article{DebKok2010tec-ged,
  author =       Deb #and# Koksalan,
  title =        {Guest Editorial: Special Issue on Preference-based
                  Multiobjective Evolutionary Algorithms},
  journal =      tec,
  volume =       14,
  number =       5,
  month =        oct,
  year =         2010,
  pages =        {669--670},
  doi =          {10.1109/TEVC.2010.2070371},
}

@Article{DebMohMis2005epsilon,
  title =        {Evaluating the {$\epsilon$}-domination based multi-objective
                  evolutionary algorithm for a quick computation of
                  {Pareto}-optimal solutions},
  author =       Deb #and# {Mohan, Manikanth} #and# {Mishra, Shikhar},
  journal =      ec,
  year =         2005,
  month =        dec,
  number =       4,
  pages =        {501--525},
  volume =       13,
  doi =          {10.1162/106365605774666895},
  keywords =     "$\epsilon$-dominance, $\epsilon$-MOEA"
}

@Article{DebTiw2008omni,
  author       = Deb #and# Tiwari_S,
  title        = {Omni-optimizer: {A} generic evolutionary algorithm for single
                  and multi-objective optimization},
  journal      = ejor,
  year         = 2008,
  volume       = 185,
  number       = 3,
  pages        = {1062--1087},
  annote       = {Archiving method with epsilon dominance and density in the
                  decision and objective spaces},
  keywords     = {epsilon-dominance, archiving},
  doi          = {10.1016/j.ejor.2006.06.042}
}

@Article{DebZhuKul2018tec,
  author =       Deb #and# {Zhu, Ling and Kulkarni, Sandeep},
  title =        {Handling Multiple Scenarios in Evolutionary Multi-Objective
                  Numerical Optimization},
  doi =          {10.1109/TEVC.2017.2776921},
  abstract =     {Solutions to most practical numerical optimization problems
                  must be evaluated for their performance over a number of
                  different loading or operating conditions, which we refer
                  here as scenarios. Therefore, a meaningful and resilient
                  optimal solution must be such that it remains feasible under
                  all scenarios and performs close to an individual optimal
                  solution corresponding to each scenario. Despite its
                  practical importance, multi-scenario consideration has
                  received a lukewarm attention, particularly in the context of
                  multi-objective optimization. The usual practice is to
                  optimize for the worst-case scenario. In this paper, we
                  review existing methodologies in this direction and set our
                  goal to suggest a new and potential population-based method
                  for handling multiple scenarios by defining scenario-wise
                  domination principle and scenario-wise diversity-preserving
                  operators. To evaluate, the proposed method is applied to a
                  number of numerical test problems and engineering design
                  problems with a detail explanation of the obtained results
                  and compared with an existing method. This first systematic
                  evolutionary based multi-scenario, multiobjective,
                  optimization study on numerical problems indicates that
                  multiple scenarios can be handled in an integrated manner
                  using an EMO framework to find a well-balanced compromise set
                  of solutions to multiple scenarios and maintain a tradeoff
                  among multiple objectives. In comparison to an existing
                  serial multiple optimization approach, the proposed approach
                  finds a set of compromised trade-off solutions
                  simultaneously. An achievement of multi-objective trade-off
                  and multi-scenario trade-off is algorithmically challenging,
                  but due to its practical appeal, further research and
                  application must be spent.},
  journal =      tec,
  year =         2018,
  volume =       22,
  number =       6,
  pages =        {920--933},
  keywords =     "scenario-based"
}

@Article{DecSor2012ejor,
  author =       {Annelies De Corte} #and# Sorensen,
  title =        {Optimisation of gravity-fed water distribution network
                  design: A critical review},
  journal =      ejor,
  volume =       228,
  number =       1,
  pages =        {1--10},
  doi =          "10.1016/j.ejor.2012.11.046",
  year =         2013,
}

@Article{DecSor2016,
  author =       {Annelies De Corte} #and# Sorensen,
  title =        {An Iterated Local Search Algorithm for Water Distribution
                  Network Design Optimization},
  journal =      networks,
  year =         2016,
  volume =       67,
  number =       3,
  pages =        {187--198}
}

@Article{DecSor2016water,
  author =       {Annelies De Corte} #and# Sorensen,
  title =        {An Iterated Local Search Algorithm for multi-period water
                  distribution network design optimization},
  journal =      {Water},
  volume =       8,
  number =       8,
  pages =        359,
  doi =          "10.3390/w8080359",
  year =         2016,
}

@Article{Dek1981:cad,
  author =       Dekhtyarenko,
  title =        "Verification of weight coefficients in multicriteria optimization problems",
  journal =      cad,
  volume =       13,
  number =       6,
  pages =        "339--344",
  year =         1981,
}

@Article{DelGanDeg2010,
  title =        {Evolutionary, constructive and hybrid procedures for
                  the bi-objective set packing problem},
  author =       {Delorme, X. } #and# Gandibleux #and# { Degoutin, F.},
  journal =      ejor,
  volume =       204,
  number =       2,
  pages =        {206--217},
  year =         2010,
  annote =       "This paper cannot be found on internet!! Does it exist?"
}

@Article{DelGarGro2012:cor,
  author =       DellaCroce #and# {Thierry Garaix} #and# Grosso,
  title =        {Iterated Local Search and Very Large Neighborhoods for the Parallel-machines
               Total Tardiness Problem},
  journal =      cor,
  year =         2012,
  volume =       39,
  number =       6,
  pages =        {1213--1217}
}

@Article{DelIorMar2016binpack,
  title =        {Bin packing and cutting stock problems: Mathematical models
                  and exact algorithms},
  author =       {Delorme, Maxence} #and# Iori #and# Martello_S,
  journal =      ejor,
  volume =       255,
  number =       1,
  pages =        {1--20},
  year =         2016,
  publisher =    {Elsevier},
  doi = 	 {10.1016/j.ejor.2016.04.030}
}

@Article{DelIorMarMon2016,
  author = 	 {Mauro Dell'Amico} #and# Iori #and# Martello_S #and# Monaci_M,
  title = 	 {Heuristic and Exact Algorithms for the Identical Parallel Machine Scheduling Problem},
  journal = 	 informsjoc,
  year = 	 2016,
  volume = 	 20,
  number = 	 3,
  pages = 	 {333--344}
}

@Article{DelIorMarc2018bpplib,
  title =        {{BPPLIB}: a library for bin packing and cutting stock problems},
  author =       {Delorme, Maxence} #and# Iori #and# Martello_S,
  journal =      ol,
  volume =       12,
  number =       2,
  pages =        {235--250},
  year =         2018,
  doi = 	 {10.1007/s11590-017-1192-z}
}

@Article{DelIorNovStu2016,
  author =       {Mauro Dell'Amico} #and# Iori #and# {Stefano Novellani} #and# Stuetzle,
  title =        "A destroy and repair algorithm for the Bike sharing Rebalancing Problem",
  journal =      cor,
  volume =       71,
  pages =        {146--162},
  year =         2016,
  doi =          {10.1016/j.cor.2016.01.011},
  keywords =     {irace},
}

@Article{DelKar1990interactive,
  title =        {An interactive {MCDM} weight space reduction method utilizing
                  a {Tchebycheff} utility function},
  author =       {Dell, Robert F. and Karwan, Mark H.},
  journal =      nrl,
  volume =       37,
  number =       2,
  pages =        {263--277},
  year =         1990,
}

@Article{DelTru1993,
  author =       {Mauro Dell'Amico and Marco Trubian},
  title =        {Applying Tabu Search to the Job Shop Scheduling Problem},
  journal =      aor,
  year =         1993,
  volume =       41,
  pages =        {231--252}
}

@Article{DemEichFli2015effects,
  title =        {On the effects of combining objectives in multi-objective
                  optimization},
  author =       {Dempe, Stephan and Eichfelder, Gabriele and Fliege, J{\"o}rg},
  journal =      mmor,
  volume =       82,
  number =       1,
  pages =        {1--18},
  year =         2015,
  publisher =    {Springer}
}

@Article{DenAroGosPas1990,
  author =       Deneubourg #and# "S. Aron and S. Goss and
                  J.-M. Pasteels",
  title =        "The Self-Organizing Exploratory Pattern of the
                  {Argentine} Ant",
  journal =      jib,
  year =         1990,
  volume =       3,
  number =       2,
  pages =        "159--168",
  doi =          {10.1007/BF01417909},
  alias =        {DAGP90},
}

@Article{DenZha2019approxhv,
  author       = {Deng, Jingda} #and# Zhang_Qingfu,
  title        = {Approximating Hypervolume and Hypervolume Contributions Using
                  Polar Coordinate},
  journal      = tec,
  year         = 2019,
  volume       = 23,
  number       = 5,
  pages        = {913--918},
  month        = oct,
  annote       = {Proposed approximating the hypervolume using scalarizations},
  doi          = {10.1109/tevc.2019.2895108}
}

@Article{DerGarMolHer2011stats,
  title =        {A practical tutorial on the use of nonparametric statistical
                  tests as a methodology for comparing evolutionary and swarm
                  intelligence algorithms},
  author =       {Derrac, Joaqu{\'i}n and Garc{\'i}a, Salvador} #and# Molina_D #and# Herrera_F,
  journal =      swevo,
  volume =       1,
  number =       1,
  pages =        {3--18},
  year =         2011,
}

@Article{DerVog2014:joh,
  author =       {Ulrich Derigs and Ulrich Vogel},
  title =        {Experience with a Framework for Developing Heuristics for
                  Solving Rich Vehicle Routing Problems},
  journal =      joh,
  year =         2014,
  volume =       20,
  number =       1,
  pages =        {75--106}
}

@Article{DesBelDop2021bops,
  author =       {Aryan Deshwal and Syrine Belakaria and Janardhan Rao Doppa
                  and Dae Hyun Kim},
  title =        {Bayesian Optimization over Permutation Spaces},
  journal =      arxiv # {2112.01049},
  year =         2021,
  doi =          "10.48550/arXiv.2112.01049",
  keywords =     "BOPS, CEGO"
}

@Article{DesRitLopPer2021acviz,
  author =       DeSouza #and# Ritt #and# Lopez-Ibanez #and# Perez_L,
  title =        {{\softwarepackage{ACVIZ}}: A Tool for the Visual Analysis of
                  the Configuration of Algorithms with {\rpackage{irace}}},
  journal =      orp,
  year =         2021,
  doi =          "10.1016/j.orp.2021.100186",
  supplement =   {https://zenodo.org/record/4714582},
  abstract =     "This paper introduces acviz, a tool that helps to analyze the
                  automatic configuration of algorithms with irace. It provides
                  a visual representation of the configuration process,
                  allowing users to extract useful information, e.g. how the
                  configurations evolve over time. When test data is available,
                  acviz also shows the performance of each configuration on the
                  test instances. Using this visualization, users can analyze
                  and compare the quality of the resulting configurations and
                  observe the performance differences on training and test
                  instances.",
  volume =       8,
  pages =        100186,
}

@Article{DetPapZab2017omega,
  title =        {A multi-depot dial-a-ride problem with heterogeneous vehicles
                  and compatibility constraints in healthcare},
  author =       "Paolo Detti and Francesco Papalini and Garazi Zabalo {Manrique
                  de Lara}",
  journal =      omega,
  volume =       70,
  pages =        {1--14},
  year =         2017,
  doi =          "10.1016/j.omega.2016.08.008"
}

@Article{DevVoh2003informs,
  title =        {Combinatorial Auctions: A Survey},
  author =       DeVries #and# Vohra,
  journal =      informsjoc,
  volume =       15,
  number =       3,
  pages =        {284--309},
  year =         2003,
  publisher =    informs
}

@Article{DiaHanXu2017,
  title =        "Evolutionary robust optimization in production planning:
                  interactions between number of objectives, sample size and
                  choice of robustness measure",
  journal =      cor,
  volume =       79,
  pages =        "266--278",
  year =         2017,
  doi =          "10.1016/j.cor.2016.06.020",
  author =       Diaz_JE #and# Handl_J #and# Xu_DL,
  keywords =     "Evolutionary multi-objective optimization, Production
                  planning, Robust optimization, Simulation-based optimization,
                  Uncertainty modelling",
  alias =        "DIAZ2017266"
}

@Article{DiaHanXu2018,
  title =        "Integrating meta-heuristics, simulation and exact techniques
                  for production planning of a failure-prone manufacturing
                  system",
  journal =      ejor,
  volume =       266,
  number =       3,
  pages =        "976--989",
  year =         2018,
  issn =         "0377-2217",
  doi =          "10.1016/j.ejor.2017.10.062",
  author =       Diaz_JE #and# Handl_J #and# Xu_DL,
  keywords =     "Genetic algorithms, Combinatorial optimization, Production
                  planning, Simulation-based optimization, Uncertainty
                  modelling",
  alias =        {DIAZ2018976},
}

@Article{DiaLop2020ejor,
  author =       Diaz_JE #and# Lopez-Ibanez,
  title =        "Incorporating Decision-Maker's Preferences into the Automatic
                  Configuration of Bi-Objective Optimisation Algorithms",
  journal =      ejor,
  year =         2021,
  volume =       289,
  number =       3,
  pages =        "1209--1222",
  doi =          "10.1016/j.ejor.2020.07.059",
  abstract =     "Automatic configuration (AC) methods are increasingly used to
                  tune and design optimisation algorithms for problems with
                  multiple objectives. Most AC methods use unary quality
                  indicators, which assign a single scalar value to an
                  approximation to the Pareto front, to compare the performance
                  of different optimisers. These quality indicators, however,
                  imply preferences beyond Pareto-optimality that may differ
                  from those of the decision maker (DM). Although it is
                  possible to incorporate DM's preferences into quality
                  indicators, e.g., by means of the weighted hypervolume
                  indicator (HV$^w$), expressing preferences in terms of weight
                  function is not always intuitive nor an easy task for a DM,
                  in particular, when comparing the stochastic outcomes of
                  several algorithm configurations. A more visual approach to
                  compare such outcomes is the visualisation of their empirical
                  attainment functions (EAFs) differences. This paper proposes
                  using such visualisations as a way of eliciting information
                  about regions of the objective space that are preferred by
                  the DM. We present a method to convert the information about
                  EAF differences into a HV$^w$ that will assign higher quality
                  values to approximation fronts that result in EAF differences
                  preferred by the DM. We show that the resulting HV$^w$ may be
                  used by an AC method to guide the configuration of
                  multi-objective optimisers according to the preferences of
                  the DM. We evaluate the proposed approach on a well-known
                  benchmark problem. Finally, we apply our approach to
                  re-configuring, according to different DM's preferences, a
                  multi-objective optimiser tackling a real-world production
                  planning problem arising in the manufacturing industry.",
  supplement =   {https://doi.org/10.5281/zenodo.3749288}
}

@Article{DiaMouFigCli2002:ejor,
  author =       Dias #and# Mousseau #and# Figueira #and# Climaco,
  title =        "An aggregation/disaggregation approach to obtain robust conclusions with {ELECTRE TRI}",
  year =         2002,
  journal =      ejor,
  volume =       138,
  number =       2,
  month =        apr,
  pages =        "332--348 ",
}

@Article{DiaYan2009small,
  title =        {Small approximate {Pareto} sets for biobjective shortest
                  paths and other problems},
  author =       {Diakonikolas, Ilias} #and# Yannakakis,
  journal =      siamc,
  year =         2009,
  number =       4,
  pages =        {1340--1371},
  volume =       39,
}

@Article{DicDor1998:jair,
  author =       DiCaro #and# Dorigo,
  title =        "Ant{Net}: Distributed Stigmergetic Control for Communications
                  Networks",
  journal =      jair,
  volume =       9,
  pages =        "317--365",
  year =         1998,
  alias =        "DicDor98:jair",
}

@Article{DicDucGam2005,
  author =       DiCaro #and# Ducatelle #and# Gambardella,
  title =        "{AntHocNet}: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks",
  journal =      eutranstel,
  year =         2005,
  volume =       16,
  number =       5,
  pages =        {443--455}
}

@Article{DigSch2003,
  author =       DiGaspero #and# Schaerf,
  citations =    36,
  journal =      spe,
  keywords =     {software engineering, local search, easylocal},
  month =        jul,
  number =       8,
  pages =        {733--765},
  publisher =    jws,
  title =        {\textsc{EasyLocal++}: An object-oriented framework
                  for flexible design of local search algorithms},
  epub =          {http://www.diegm.uniud.it/satt/papers/DiSc03.pdf},
  volume =       33,
  year =         2003
}

@Article{DilKhaNem2017comments,
  title =        {Comments on: On learning and branching: a survey},
  author =       {Dilkina, Bistra and Khalil, Elias B. and Nemhauser, George
                  L.},
  journal =      {TOP},
  volume =       25,
  pages =        {242--246},
  year =         2017,
  publisher =    {Springer},
  annote =       "Comments on \cite{LodZar2017learning}."
}

@Article{DinDonHeLi2019twoarch,
  title =        {A novel two-archive strategy for evolutionary many-objective
                  optimization algorithm based on reference points},
  author =       {Ding, Rui and Dong, Hongbin and He, Jun and Li, Tao},
  journal =      asoco,
  year =         2019,
  pages =        {447--464},
  volume =       78,
  doi =          "10.1016/j.asoc.2019.02.040",
  publisher =    {Elsevier}
}

@Article{DinSonGup2015,
  author = 	 {Ding, J.-Y. and Song, S. and Gupta, J. N. D. and Zhang, R. and Chiong, R. and Wu, C.},
  title = 	 {An Improved Iterated Greedy Algorithm with a Tabu-based Reconstruction Strategy for the No-wait Flowshop Scheduling Problem},
  journal = 	 asoco,
  year = 	 2015,
  volume = 	 30,
  pages = 	 {604--613}
}

@Article{DoeDoeEbe2015,
  author =       Doerr_B #and# Doerr_C #and# {Franziska Ebel},
  title =        {From black-box complexity to designing new genetic algorithms},
  journal =      tcs,
  volume =       567,
  pages =        {87--104},
  year =         2015,
  doi =          {10.1016/j.tcs.2014.11.028}
}

@Article{DoeDoeYan2020,
  author =       Doerr_B #and# Doerr_C #and# {Yang, Jing},
  title =        {Optimal parameter choices via precise black-box analysis},
  journal =      tcs,
  volume =       801,
  pages =        {1--34},
  year =         2020,
  doi =          {10.1016/j.tcs.2019.06.014},
}

@Article{DoeFueGro06,
  author =       Doerner #and# Fuellerer #and# Gronalt #and# Hartl #and# Iori,
  title =        {Metaheuristics for the Vehicle Routing Problem with Loading Constraints},
  journal =      networks,
  year =         2006,
  volume =       49,
  number =       4,
  pages =        {294--307},
}

@Article{DoeGieWitYan2019,
  title =        {The ({1+\(\lambda\)}) evolutionary
                  algorithm with self-adjusting mutation rate},
  author =       Doerr_B #and# {Gie{\ss}en, Christian} #and# Witt #and# {Yang, Jing},
  journal =      algorithmica,
  volume =       81,
  number =       2,
  pages =        {593--631},
  year =         2019,
  publisher =    Springer
}

@Article{DoeGutHar08,
  author =       Doerner #and# Gutjahr #and# Hartl #and# Strauss #and#
                  Stummer,
  title =        {Nature-Inspired Metaheuristics in Multiobjective
                  Activity Crashing},
  journal =      omega,
  year =         2008,
  volume =       36,
  number =       6,
  pages =        {1019--1037},
}

@Article{DoeGutHarStrStu04:aor,
  author =       Doerner #and# Gutjahr #and# Hartl #and# Strauss #and#
                  Stummer,
  title =        {{Pareto} Ant Colony Optimization: A Metaheuristic
                  Approach to Multiobjective Portfolio Selection},
  journal =      aor,
  year =         2004,
  volume =       131,
  pages =        {79--99},
  alias =        {DoeGutHarStrStu03}
}

@Article{DoeGutHarStrStu06:ejor,
  author =       Doerner #and# Gutjahr #and# Hartl #and# Strauss #and#
                  Stummer,
  title =        {{Pareto} ant colony optimization with ILP preprocessing in
                   multiobjective project portfolio selection},
  journal =      ejor,
  year =         2006,
  volume =       171,
  pages =        {830--841},
}

@Article{DoeHarRei03,
  author    =    Doerner #and# Hartl #and# Reimann,
  title     =    "Are {COMPETants} more competent for problem solving?
                  {The} case of a multiple objective transportation
                  problem",
  journal   =    cejore,
  pages     =    "115--141",
  volume    =    11,
  number    =    2,
  year      =    2003,
}

@Article{DoeJohWin2012,
  title =        {Multiplicative drift analysis},
  author =       Doerr_B #and# {Johannsen, Daniel and Winzen, Carola},
  journal =      algorithmica,
  volume =       64,
  number =       4,
  pages =        {673--697},
  year =         2012
}

@Article{DoeKotLenWin2013,
  title =        {Black-box complexities of combinatorial problems},
  author =       Doerr_B #and# Kotzing_T #and# {Lengler, Johannes and Winzen, Carola},
  journal =      tcs,
  volume =       471,
  pages =        {84--106},
  year =         2013
}

@Article{DoeMerStu2009:swarm,
  author =       Doerner #and# Merkle #and# Stuetzle,
  title =        {Special issue on Ant Colony Optimization},
  journal =      swarm,
  year =         2009,
  volume =       3,
  number =       1,
}

@Article{DoeNeuSudWit2011:tcs,
  author =       Doerr_B #and# Neumann_F #and# Sudholt #and# Witt,
  title =        {Runtime analysis of the 1-{ANT} ant colony optimizer},
  journal =      tcs,
  year =         2011,
  volume =       412,
  number =       1,
  pages =        {1629--1644}
}

@Article{Dog2015asoco,
  author =       Dogan,
  title =        {Composite artificial bee colony algorithms: From
                  component-based analysis to high-performing algorithms},
  journal =      asoco,
  volume =       32,
  pages =        {266--285},
  year =         2015,
  doi =          "10.1016/j.asoc.2015.03.051",
  keywords =     {irace},
}

@Article{DoiPekReg2004rank,
  author =       {Jean-Paul Doignon and Aleksandar Peke{\v{c}} and Michel
                  Regenwetter},
  title =        {The repeated insertion model for rankings: Missing link
                  between two subset choice models},
  doi =          {10.1007/bf02295838},
  year =         2004,
  month =        mar,
  volume =       69,
  number =       1,
  pages =        {33--54},
  journal =      {Psychometrika},
  abstract =     "Several probabilistic models for subset choice have been
                  proposed in the literature, for example, to explain approval
                  voting data. We show that Marley et al.'s latent scale model
                  is subsumed by Falmagne and Regenwetter's size-independent
                  model, in the sense that every choice probability
                  distribution generated by the former can also be explained by
                  the latter. Our proof relies on the construction of a
                  probabilistic ranking model which we label the ``repeated
                  insertion model''. This model is a special case of Marden's
                  orthogonal contrast model class and, in turn, includes the
                  classical Mallows $\varphi$-model as a special case. We
                  explore its basic properties as well as its relationship to
                  Fligner and Verducci's multistage ranking model."
}

@Article{DolMor2002benchmarking,
  author =       {Dolan, Elizabeth D. and Mor{\'e}, Jorge J.},
  journal =      mp,
  number =       2,
  pages =        {201--213},
  title =        {Benchmarking optimization software with performance profiles},
  volume =       91,
  year =         2002,
  keywords =     "performance profiles; convergence",
  annote = "This methodology has been criticized in \url{https://doi.org/10.1145/2950048}"
}

@Article{DonCheHua2013,
  author =       {Xingye Dong and Ping and Houkuan Huang and Maciek Nowak},
  title =        {A Multi-restart Iterated Local Search Algorithm for the Permutation Flow Shop Problem Minimizing Total Flow Time},
  journal =      cor,
  year =         2013,
  volume =       40,
  number =       2,
  pages =        {627--632}
}

@Article{DonHuaChe2009:cor,
  author =       {X. Dong and H. Huang and P. Chen},
  title =        {An Iterated Local Search Algorithm for the Permutation Flowshop Problem with Total Flowtime Criterion},
  journal =      cor,
  year =         2009,
  volume =       36,
  number =       5,
  pages =        {1664--1669}
}

@Article{DonMonCasRizGam08,
  author =       {A. V. Donati} #and# Montemanni_R #and# {N. Casagrande } #and#
                  {A. E. Rizzoli } #and# Gambardella,
  title =        {Time dependent vehicle routing problem with a multi ant
                  colony system},
  journal =      ejor,
  volume =       185,
  number =       3,
  year =         2008,
  pages =        {1174--1191},
}

@Article{Dor2007:scholarpedia,
  author =       Dorigo,
  title =        {Ant {Colony} {Optimization}},
  year =         2007,
  journal =      scholarpedia,
  volume =       2,
  number =       3,
  pages =        1461,
  doi =          {10.4249/scholarpedia.1461},
  alias =        {Dor2007:sch-aco}
}

@Article{Dor2016sipolicy,
  author =       Dorigo,
  title =        "Swarm intelligence: A few things you need to know if you want
                  to publish in this journal",
  journal =      swarm,
  year =         2016,
  month =        nov,
  url =
                  "https://static.springer.com/sgw/documents/1593723/application/pdf/Additional_submission_instructions.pdf"
}

@Article{DorBirLiLop2017si,
  author =       Dorigo#and# Birattari#and# Li_X #and# Lopez-Ibanez #and#
                  Ohkura_K #and# Pinciroli #and# Stuetzle,
  title =        "{ANTS} 2016 Special Issue: Editorial",
  journal =      swarm,
  year =         2017,
  month =        nov,
  doi =          "10.1007/s11721-017-0146-5",
}

@Article{DorBirStu06:ci,
  author =       Dorigo #and#  Birattari #and# Stuetzle,
  title =        {Ant Colony Optimization: Artificial Ants as a Computational Intelligence Technique},
  journal =      cim,
  year =         2006,
  volume =       1,
  number =       4,
  pages =        {28--39}
}

@Article{DorBlu2005:tcs,
  author =       Dorigo #and#Blum,
  title =        {Ant colony optimization theory: A survey},
  journal =      tcs,
  volume =       344,
  number =       {2-3},
  year =         2005,
  pages =        {243--278},
}

@Article{DorDicGam99:al,
  author =       Dorigo #and# DiCaro #and# Gambardella,
  title =        "Ant Algorithms for Discrete Optimization",
  journal =      al,
  volume =       5,
  number =       2,
  pages =        "137--172",
  year =         1999,
}

@Article{DorGam1997:biosys,
  author =       Dorigo #and# Gambardella,
  title =        "Ant Colonies for the Traveling Salesman Problem",
  journal =      biosys,
  year =         1997,
  volume =       43,
  number =       2,
  pages =        "73--81",
  alias =        "DorGam1997:bio",
  doi = "10.1016/S0303-2647(97)01708-5"
}

@Article{DorGam1997:tec,
  key =          "DorGam97:tec",
  alias =        "DorGam97:tec",
  author =       Dorigo #and# Gambardella,
  title =        "{Ant} {Colony} {System}: A Cooperative Learning
                  Approach to the Traveling Salesman Problem",
  journal =      tec,
  year =         1997,
  volume =       1,
  number =       1,
  pages =        "53--66",
  keywords =      {Ant Colony System},
}

@Article{DorGamMidStu2002:tec,
  doi =          {10.1109/TEVC.2002.802446},
  author =       Dorigo #and# Gambardella #and# Middendorf #and#
                  Stuetzle,
  title =        {Guest Editorial: Special Section on Ant Colony
                  Optimization},
  year =         2002,
  journal =      tec,
  volume =       6,
  number =       4,
  pages =        {317--320},
  keywords =      {ant colony optimization, swarm intelligence}
}

@Article{DorManCol1996:tsmcb,
  author =       Dorigo #and# Maniezzo #and# Colorni,
  title =        "{Ant} {System}: Optimization by a Colony of
                  Cooperating Agents",
  journal =      tsmcb,
  year =         1996,
  volume =       26,
  number =       1,
  pages =        "29--41",
  alias =        {DorManCol96:tsmcb},
}

@Article{DorStuDic2000:fgcs,
  author =       Dorigo #and# Stuetzle #and# DiCaro,
  title =        "Special Issue on ``{Ant} {Algorithms}''",
  year =         2000,
  journal =      fgcs,
  volume =       16,
  number =       8,
  keywords =      {swarm intelligence, ant colony optimization}
}

@Article{DouZop2010:ejor,
  author =       Doumpos #and# Zopounidis,
  title =        "Preference disaggregation and statistical learning for multicriteria decision support: A review",
  journal =      ejor,
  volume =       209,
  number =       3,
  pages =        "203--214",
  year =         2011
}

@Article{DovTusFil2010ec,
  author =       {Erik Dovgan} #and# Tusar #and# Filipic,
  title =        {Parameter tuning in an evolutionary algorithm for commodity
                  transportation optimization},
  journal =      ec,
  alias =        {ParamTunComm},
  year =         2010,
  pages =        {1--8}
}

@Article{DreSia2004,
  author =       Dreo_J #and# {P. Siarry},
  title =        {Continuous interacting ant colony algorithm based on dense
                  heterarchy},
  journal =      fgcs,
  volume =       20,
  number =       5,
  year =         2004,
  pages =        {841--856},
  alias =        {DreSia04}
}

@Article{DroJanWeg2006,
  title =        {Upper and lower bounds for randomized search heuristics in black-box optimization},
  author =       {Droste, Stefan and Jansen, Thomas} #and# Wegener,
  journal =      tocs,
  volume =       39,
  number =       4,
  pages =        {525--544},
  year =         2006,
  publisher =    springer
}

@Article{DruThi2012,
   author =  Drugan #and# Thierens,
   title =   {Stochastic {Pareto} local search: {Pareto} neighbourhood
              exploration and perturbation strategies},
   journal = joh,
   volume =  18,
   number =   5,
   year =    2012,
   pages =   {727--766}
}

@Article{DuLeu90,
  author =       Du_J #and# Leung_JYT,
  title =        "Minimizing Total Tardiness on One Machine is
                  {NP}-Hard",
  journal =      mor,
  year =         1990,
  volume =       15,
  number =       3,
  pages =        {483--495}
}

@Article{DubLopStu2011amai,
  author =       Dubois-Lacoste #and# Lopez-Ibanez #and# Stuetzle,
  title =        "Improving the Anytime Behavior of Two-Phase Local
                  Search",
  journal =      amai,
  year =         2011,
  volume =       61,
  number =       2,
  pages =        {125--154},
  doi =          {10.1007/s10472-011-9235-0},
  alias =        {DubLopStu2010amai},
  pdf = {DubLopStu2011amai.pdf},
}

@Article{DubLopStu2011cor,
  author =       Dubois-Lacoste #and# Lopez-Ibanez #and# Stuetzle,
  title =        "A Hybrid {TP$+$PLS} Algorithm for Bi-objective
                  Flow-Shop Scheduling Problems",
  journal =      cor,
  year =         2011,
  volume =       38,
  number =       8,
  pages =        {1219--1236},
  doi =          {10.1016/j.cor.2010.10.008},
  supplement =   {http://iridia.ulb.ac.be/supp/IridiaSupp2010-001/},
  pdf =       {DubLopStu2011cor.pdf},
}

@Article{DubLopStu2015ejor,
  author =       Dubois-Lacoste #and# Lopez-Ibanez #and# Stuetzle,
  title =        {Anytime {Pareto} Local Search},
  journal =      ejor,
  year =         2015,
  volume =       243,
  number =       2,
  pages =        {369--385},
  doi =          {10.1016/j.ejor.2014.10.062},
  pdf =          "DubLopStu2015ejor.pdf",
  alias =        {DubLopStu2013cor},
  keywords =     "Pareto local search",
}

@Article{DubPagStu2017cor,
  author =       Dubois-Lacoste #and# Pagnozzi #and# Stuetzle,
  title =        {An Iterated Greedy Algorithm with Optimization of Partial
  Solutions for the Permutation Flowshop Problem},
  journal =      cor,
  year =         2017,
  supplement =   {http://iridia.ulb.ac.be/supp/IridiaSupp2013-006},
  volume =       81,
  pages  =       {160--166},
  doi    =       {10.1016/j.cor.2016.12.021}
}

@Article{Dud2008carbodies,
  author =       {Fabian Duddeck},
  title =        {Multidisciplinary optimization of car bodies},
  journal =      smo,
  year =         2008,
  volume =       35,
  number =       4,
  pages =        {375--389},
  doi =          {10.1007/s00158-007-0130-6},
  annote = "Evolutionary optimization of car bodies at General Motors"
}
@Article{DueSch90,
  author =       Dueck_G #and# {T. Scheuer},
  title =        {Threshold Accepting: A General Purpose Optimization
                  Algorithm Appearing Superior to Simulated Annealing},
  journal =      jcp,
  year =         1990,
  volume =       90,
  number =       1,
  pages =        {161--175}
}

@Article{Dueck1993,
  title =        {New Optimization Heuristics: the Great Deluge Algorithm and the Record-To-Record Travel},
  author =       Dueck_G,
  journal =      jcp,
  volume =       104,
  number =       1,
  pages =        {86--92},
  year =         1993,
  publisher =    elsevier
}

@Article{DuiBerCar2017constrained,
  title =        "Constrained {Bayesian} Optimization with Particle Swarms for
                  Safe Adaptive Controller Tuning",
  journal =      ifac,
  volume =       50,
  number =       1,
  pages =        "11800--11807",
  year =         2017,
  annote =       "20th IFAC World Congress",
  doi =          "10.1016/j.ifacol.2017.08.1991",
  author =       "Rikky R. P. R. Duivenvoorden and Felix Berkenkamp and Nicolas
                  Carion and Andreas Krause and Angela P. Schoellig",
  keywords =     "Adaptive Control, Constrained Bayesian Optimization, Safety,
                  Gaussian Process, Particle Swarm Optimization, Policy Search,
                  Reinforcement learning",
  abstract =     "Tuning controller parameters is a recurring and
                  time-consuming problem in control. This is especially true in
                  the field of adaptive control, where good performance is
                  typically only achieved after significant tuning. Recently,
                  it has been shown that constrained Bayesian optimization is a
                  promising approach to automate the tuning process without
                  risking system failures during the optimization
                  process. However, this approach is computationally too
                  expensive for tuning more than a couple of parameters. In
                  this paper, we provide a heuristic in order to efficiently
                  perform constrained Bayesian optimization in high-dimensional
                  parameter spaces by using an adaptive discretization based on
                  particle swarms. We apply the method to the tuning problem of
                  an L1 adaptive controller on a quadrotor vehicle and show
                  that we can reliably and automatically tune parameters in
                  experiments."
}

@Article{DuiVos1999,
  author =       {Cees Duin} #and# Voss_S,
  title =        {The Pilot Method: A Strategy for Heuristic Repetition with Application to the {Steiner} Problem in Graphs},
  journal =      networks,
  year =         1999,
  volume =       34,
  number =       3,
  pages =        {181--191}
}

@Article{Dumas95tsptw,
  author =       Dumas #and# Desrosiers #and# Gelinas_E #and# Solomon,
  title =        {An Optimal Algorithm for the Traveling Salesman Problem with
                  Time Windows},
  journal =      or,
  year =         1995,
  volume =       43,
  number =       2,
  pages =        {367--371},
  doi =          {10.1287/opre.43.2.367}
}

@Article{Dun1964tech,
  title =        {Multiple Comparisons Using Rank Sums},
  author =       Dunn,
  journal =      techms,
  volume =       6,
  number =       3,
  pages =        {241--252},
  year =         1964,
  publisher =    {Taylor \& Francis Group}
}

@Article{Dunn1961jasa,
  title =        {Multiple Comparisons Among Means},
  author =       dunn,
  journal =      jasa,
  volume =       56,
  number =       293,
  pages =        {52--64},
  year =         1961,
  publisher =    {Taylor \& Francis Group}
}

@Article{DurNeb2011jmetal,
  title =        "{jMetal}: A {Java} framework for multi-objective
                  optimization",
  journal =      aes,
  volume =       42,
  number =       10,
  pages =        "760--771",
  year =         2011,
  issn =         "0965-9978",
  doi =          "10.1016/j.advengsoft.2011.05.014",
  author =       Durillo_JJ #and# Nebro,
}

@Article{EggLinHut2019jair,
  title =        {Pitfalls and best practices in algorithm configuration},
  author =       Eggensperger_K #and# Lindauer_M #and# Hutter,
  journal =      jair,
  volume =       64,
  pages =        {861--893},
  year =         2019
}

@Article{Eglese1990,
  title =        {Simulated Annealing: a Tool for Operational Research},
  author =       Eglese_RW,
  journal =      ejor,
  volume =       46,
  number =       3,
  pages =        {271--281},
  year =         1990,
  publisher =    elsevier
}

@Article{EhmCamTho2016tdvrp,
  author       = {Ehmke, Jan Fabian and Campbell, Ann Melissa} #and# Thomas_BW,
  title        = {Vehicle routing to minimize time-dependent emissions in urban
                  areas},
  journal      = ejor,
  year         = 2016,
  volume       = 251,
  number       = 2,
  pages        = {478--494},
  month        = jun,
  doi          = {10.1016/j.ejor.2015.11.034}
}

@Article{Ehr2006discussion,
  title =        {A discussion of scalarization techniques for multiple
                  objective integer programming},
  author =       Ehrgott,
  journal =      aor,
  volume =       147,
  number =       1,
  pages =        {343--360},
  year =         2006,
  publisher =    {Springer}
}

@Article{EhrGan04,
  author =       Ehrgott #and# Gandibleux,
  title =        "Approximative Solution Methods for Combinatorial
                  Multicriteria Optimization",
  journal =      {TOP},
  year =         2004,
  volume =       12,
  number =       1,
  pages =        {1--88}
}

@Article{EhrKla1997connectedness,
  author =       Ehrgott #and# Klamroth,
  journal =      ejor,
  number =       1,
  pages =        {159--166},
  title =        {Connectedness of Efficient Solutions in Multiple Criteria
                  Combinatorial Optimization},
  volume =       97,
  year =         1997,
  doi =          {10.1016/S0377-2217(96)00116-6}
}

@Article{EibHinMich1999tec,
  author =       Eiben #and# {Robert Hinterding} #and# Michalewicz,
  title =        {Parameter Control in Evolutionary Algorithms},
  journal =      tec,
  volume =       3,
  number =       2,
  pages =        {124--141},
  year =         1999
}

@Article{EibRud1999,
  title =        {Theory of evolutionary algorithms: A bird's eye view},
  author =       Eiben #and# Rudolph_G,
  journal =      tcs,
  volume =       229,
  number =       {1-2},
  pages =        {3--9},
  year =         1999
}

@Article{EibSmi2011swec,
  author =       Eiben #and# Smit_SK,
  title =        {Parameter Tuning for Configuring and Analyzing
                  Evolutionary Algorithms},
  journal =      swevo,
  year =         2011,
  volume =       1,
  number =       1,
  pages =        {19--31},
  doi =          {10.1016/j.swevo.2011.02.001}
}

@Article{EkeKwa2018ems,
  title =        {Including robustness considerations in the search phase of
                  Many-Objective Robust Decision Making},
  author =       {Eker, Sibel} #and# Kwakkel,
  journal =      ems,
  volume =       105,
  pages =        {201--216},
  year =         2018,
  keywords =     "scenario-based",
}

@Article{Elm1991rnn,
  title =        {Distributed representations, simple recurrent networks, and
                  grammatical structure},
  author =       {Elman, Jeffrey L},
  journal =      ml,
  volume =       7,
  number =       {2-3},
  pages =        {195--225},
  year =         1991,
  publisher =    {Springer}
}

@Article{EmePer1991,
  author =       {V. A. Emelichev and V. A. Perepelitsa},
  title =        {Complexity of Vector Optimization Problems on Graphs},
  journal =      optimization,
  year =         1991,
  volume =       22,
  number =       6,
  doi =          {10.1080/02331939108843732},
  pages =        {906--918}
}

@Article{EmePer1992,
  author =       {V. A. Emelichev and V. A. Perepelitsa},
  title =        {On the Cardinality of the Set of Alternatives in Discrete Many-criterion Problems},
  journal =      dma,
  year =         1992,
  volume =       2,
  number =       5,
  pages =        {461--471},
}

@Article{EmmDeuw2018tutorial,
  title =        {A tutorial on multiobjective optimization: Fundamentals and
                  evolutionary methods},
  author =       Emmerich #and# Deutz_AH,
  journal =      natcomp,
  year =         2018,
  number =       3,
  pages =        {585--609},
  volume =       17,
  publisher =    {Springer}
}

@Article{EmmGiaNau2006,
  author =       Emmerich #and# {K. C. Giannakoglou} #and# Naujoks,
  journal =      tec,
  title =        {Single- and multiobjective evolutionary optimization assisted
                  by {Gaussian} random field metamodels},
  year =         2006,
  volume =       10,
  number =       4,
  pages =        {421--439},
  doi =          {10.1109/TEVC.2005.859463}
}

@Article{EngWie2007twod,
  title =        {{2D} decision-making for multicriteria design optimization},
  author =       {Engau, Alexander} #and# Wiecek_M,
  journal =      smo,
  volume =       34,
  pages =        {301--315},
  year =         2007,
  doi =          "10.1007/s00158-006-0078-y",
  publisher =    {Springer}
}

@Article{EngWie2008interactive,
  title =        {Interactive coordination of objective decompositions in
                  multiobjective programming},
  author =       {Engau, Alexander} #and# Wiecek_M,
  journal =      ms,
  volume =       54,
  number =       7,
  pages =        {1350--1363},
  year =         2008,
  publisher =    informs
}

@Article{EssMatDau2008,
  author =       {Imen Essafi and Yazid Mati} #and# Dauzere,
  title =        {A Genetic Local Search Algorithm for Minimizing Total Weighted
  Tardiness in the Job-shop Scheduling Problem},
  journal =      cor,
  year =         2008,
  volume =       35,
  number =       8,
  pages =        {2599--2616}
}

@Article{FanBif2013,
  author       = {Fan, Wei and Bifet, Albert},
  title        = {Mining big data: current status, and forecast to the future},
  journal      = sigkddnews,
  year         = 2013,
  volume       = 14,
  number       = 2,
  pages        = {1--5},
  publisher    = {ACM}
}

@Article{Fanelli2012,
  author =       {Fanelli, Daniele},
  title =        "Negative results are disappearing from most disciplines and
                  countries",
  journal =      {Scientometrics},
  year =         2012,
  volume =       90,
  number =       3,
  pages =        {891--904},
  doi =          {10.1007/s11192-011-0494-7},
  abstract =     "Concerns that the growing competition for funding and
                  citations might distort science are frequently discussed, but
                  have not been verified directly. Of the hypothesized
                  problems, perhaps the most worrying is a worsening of
                  positive-outcome bias. A system that disfavours negative
                  results not only distorts the scientific literature directly,
                  but might also discourage high-risk projects and pressure
                  scientists to fabricate and falsify their data. This study
                  analysed over 4,600 papers published in all disciplines
                  between 1990 and 2007, measuring the frequency of papers
                  that, having declared to have ``tested'' a hypothesis,
                  reported a positive support for it. The overall frequency of
                  positive supports has grown by over 22{\%} between 1990 and
                  2007, with significant differences between disciplines and
                  countries. The increase was stronger in the social and some
                  biomedical disciplines. The United States had published, over
                  the years, significantly fewer positive results than Asian
                  countries (and particularly Japan) but more than European
                  countries (and in particular the United
                  Kingdom). Methodological artefacts cannot explain away these
                  patterns, which support the hypotheses that research is
                  becoming less pioneering and/or that the objectivity with
                  which results are produced and published is decreasing.",
}

@Article{FarBinResFal2005tps,
  author =       Faria_H #and# "S. Binato" #and# Resende #and# Falcao_DJ,
  title =        {Power transmission network design by a greedy randomized
                  adaptive path relinking approach},
  journal =      ieeetps,
  year =         2005,
  volume =       20,
  number =       1,
  pages =        {43--49}
}

@Article{FarMarYan2015pltoolbox,
  author       = {Farrugia, Vincent E. and Mart{\'i}nez, H{\'e}ctor P. and
                  Yannakakis, Georgios N.},
  title        = {The Preference Learning Toolbox},
  journal      = arxiv # {1506.01709},
  year         = 2015,
  doi          = {10.48550/arXiv.1506.01709}
}

@Article{FarWalSav2006hydroinf,
  author =       {R. Farmani} #and# Walters #and# Savic,
  title =        "Evolutionary multi-objective optimization of the
                  design and operation of water distribution network:
                  total cost vs. reliability vs. water quality",
  journal =      hydroinf,
  year =         2006,
  volume =       8,
  number =       3,
  pages =        {165--179},
}

@Article{FavMorPel06tsptw,
  author =       Favaretto #and# Moretti #and# Pellegrini,
  title =        {Ant colony system approach for variants of the
                  traveling salesman problem with time windows},
  journal =      jios,
  year =         2006,
  volume =       27,
  number =       1,
  pages =        {35--54},
  pdf =          {pellegrini-tsptw.pdf}
}

@Article{FavMorPel07,
  author =       Favaretto #and# Moretti #and# Pellegrini,
  title =        {Ant Colony System for a {VRP} with Multiple Time Windows and Multiple Visits},
  journal =      jim,
  year =         2007,
  volume =       10,
  number =       2,
  pages =        {263--284}}

@Article{FawHoos2015ablation,
  title =        {Analysing Differences Between Algorithm Configurations
                  through Ablation},
  author =       Fawcett #and# Hoos,
  journal =      joh,
  pages =        {431--458},
  volume =       22,
  number =       4,
  year =         2016,
}

@Article{FeoRes1989,
  author       = Feo #and# Resende,
  title        = {A Probabilistic Heuristic for a Computationally Difficult Set
                  Covering Problem},
  journal      = orl,
  year         = 1989,
  volume       = 8,
  number       = 2,
  pages        = {67--71},
  annote       = "Proposed GRASP"
}

@Article{FeoRes1995,
  author =       Feo #and# Resende,
  title =        {Greedy Randomized Adaptive Search Procedures},
  journal =      jgo,
  year =         1995,
  volume =       6,
  number =       2,
  pages =        {109--113},
  alias =        {FR95},
}

@Article{FeoResSmi1994,
  author =       Feo #and# Resende #and# {S. H. Smith},
  title =        {A Greedy Randomized Adaptive Search Procedure for Maximum
                  Independent Set},
  journal =      or,
  year =         1994,
  volume =       42,
  pages =        {860--878},
  month =        oct,
  keywords =     "GRASP"
}

@Article{FerFra2014,
  title =        {On Insertion Tie-breaking Rules in Heuristics for the Permutation Flowshop Scheduling Problem},
  author =       Fernandez-Viagas #and# Framinan,
  journal =      cor,
  year =         2014,
  pages =        {60--67},
  volume=        45
}

@Article{FerFra2017,
  author = 	 Fernandez-Viagas #and# Framinan,
  title = 	 {A Beam-search-based Constructive Heuristic for the {PFSP} to Minimise
               Total Flowtime},
  journal = 	 cor,
  year = 	 2017,
  volume = 	 81,
  pages = 	 {167--177}
}

@Article{FerFra2018,
  author = 	 Fernandez-Viagas #and# Framinan,
  title = 	 {Iterated-greedy-based algorithms with beam search initialization for the permutation flowshop to minimise total tardiness},
  journal = 	 eswa,
  year = 	 2018,
  volume = 	 94,
  pages = 	 {58--69}
}

@Article{FerGarAl2016mpe,
  author =       Ferrer_J #and# Garcia-Nieto #and# Alba_E #and# Chicano_F,
  doi =          {10.1155/2016/3871046},
  journal =      mpe,
  pages =        {1--19},
  title =        {Intelligent Testing of Traffic Light Programs: Validation in
                  Smart Mobility Scenarios},
  volume =       2016,
  year =         2016
}

@Article{FerGuiRamJua2016,
  author = 	 {Alberto Ferrer and Daniel Guimarans} #and# Ramalhinho_Lourenco #and# {Angel A. Juan},
  title = 	 {A {BRILS} Metaheuristic for Non-smooth Flow-shop Problems with Failure-risk
               Costs},
  journal = 	 eswa,
  year = 	 2016,
  volume = 	 44,
  pages = 	 {177--186}
}

@Article{FerLopAlb2019asoc,
  author =       Ferrer_J #and# Lopez-Ibanez #and# Alba_E,
  title =        {Reliable Simulation-Optimization of Traffic Lights in a
                  Real-World City},
  journal =      asoco,
  year =         2019,
  volume =       78,
  pages =        "697--711",
  doi =          {10.1016/j.asoc.2019.03.016},
  pdf =          "FerLopAlb2019asoc.pdf",
  supplement =   "https://github.com/MLopez-Ibanez/irace-sumo"
}

@Article{FerNavBer2009:ejor,
  author =       Fernandez_E #and# Navarro_Jorge #and# Bernal,
  title =        "Multicriteria Sorting Using a Valued Indifference Relation Under a Preference Disaggregation Paradigm",
  journal =      ejor,
  volume =       198,
  number =       2,
  pages =        "602--609",
  year =         2009
}

@Article{FerRuiFra2016,
  author    = Fernandez-Viagas #and# Ruiz_Ruben #and# Framinan,
  title     = {A New Vision of Approximate Methods for the Permutation Flowshop to Minimise Makespan: State-of-the-art and Computational Evaluation},
  journal   = ejor,
  volume =       257,
  number =       3,
  pages =        {707--721},
  year      = 2017,
}

@Article{FerUrr2010do,
  author =       {R. {Ferreira da Silva} and S. Urrutia},
  title =        "A {General} {VNS} Heuristic for the Traveling Salesman
                  Problem with Time Windows",
  journal =      do,
  year =         2010,
  volume =       7,
  number =       4,
  pages =        {203--211},
  keywords =     {TSPTW, GVNS},
  doi = "10.1016/j.disopt.2010.04.002"
}

@Article{FerValFra2018,
  author   = Fernandez-Viagas #and# Valente #and# Framinan,
  title    = {Iterated-greedy-based algorithms with Beam Search Initialization for the Permutation Flowshop to Minimise Total Tardiness},
  journal  = eswa,
  volume   = 94,
  pages    = {58--69},
  year     = 2018,
}

@Article{FiaDaCSchSeb2010:amai,
  author =       Fialho_A #and# {Luis {Da Costa}} #and# Schoenauer #and# Sebag,
  title =        {Analyzing Bandit-based Adaptive Operator Selection
                  Mechanisms},
  journal =      amai,
  year =         2010,
  volume =       60,
  number =       {1--2},
  pages =        {25--64}
}

@Article{Fie2000:siamo,
  author =   Fielding_M,
  title =    {Simulated Annealing with an Optimal Fixed Temperature},
  journal =  siamo,
  year =     2000,
  volume =   11,
  number =   2,
  pages =    {289--307}
}

@Article{FieEveSing2003tec,
  title =        {Using unconstrained elite archives for multiobjective
                  optimization},
  author =       Fieldsend #and# Everson_R #and# {Singh, Sameer},
  journal =      tec,
  volume =       7,
  number =       3,
  pages =        {305--323},
  year =         2003,
  doi =          "10.1109/TEVC.2003.810733"
}

@Article{FigFonHalKla2017easy,
  title =        {Easy to say they are Hard, but Hard to see they are
                  Easy-Towards a Categorization of Tractable Multiobjective
                  Combinatorial Optimization Problems},
  author =       Figueira #and# Fonseca #and# {Halffmann, Pascal} #and#
                  Klamroth #and# Paquete #and# {Ruzika, Stefan and Schulze,
                  Britta and Stiglmayr, Michael and Willems, David},
  journal =      jmcda,
  volume =       24,
  number =       {1-2},
  pages =        {82--98},
  year =         2017,
  publisher =    {Wiley Online Library},
  doi =          "10.1002/mcda.1574",
}

@Article{FisBar2020improv,
  author       = Fischbach #and# Bartz-Beielstein,
  title        = {Improving the reliability of test functions generators},
  journal      = asoco,
  year         = 2020,
  volume       = 92,
  pages        = 106315
}

@Article{FisGloLod2005,
  title =        {The feasibility pump},
  author =       Fischetti #and# Glover_F #and# Lodi,
  journal =      mp,
  volume =       104,
  number =       1,
  pages =        {91--104},
  year =         2005,
  publisher =    springer
}

@Article{FisLod2003:mp,
  author =       Fischetti #and# Lodi,
  title =        {Local Branching},
  journal =      mpb,
  year =         2003,
  volume =       98,
  pages =        {23--47}
}

@Article{FisMon2014joh,
  title =        {Proximity search for 0-1 mixed-integer convex programming},
  author =       Fischetti #and# Monaci_M,
  journal =      joh,
  volume =       20,
  number =       6,
  pages =        {709--731},
  year =         2014,
  publisher =    springer
}

@Article{FisMon2014or,
  author =       Fischetti #and# Monaci_M,
  title =        {Exploiting Erraticism in Search},
  journal =      or,
  volume =       62,
  number =       1,
  pages =        {114--122},
  year =         2014,
  doi =          {10.1287/opre.2013.1231},
  annote =       {\url{http://mat.tepper.cmu.edu/blog/?p=1695}},
  abstract =     { High sensitivity to initial conditions is generally viewed
                  as a drawback of tree search methods because it leads to
                  erratic behavior to be mitigated somehow. In this paper we
                  investigate the opposite viewpoint and consider this behavior
                  as an opportunity to exploit. Our working hypothesis is that
                  erraticism is in fact just a consequence of the exponential
                  nature of tree search that acts as a chaotic amplifier, so it
                  is largely unavoidable. We propose a bet-and-run approach to
                  actually turn erraticism to one's advantage. The idea is to
                  make a number of short sample runs with randomized initial
                  conditions, to bet on the "most promising" run selected
                  according to certain simple criteria, and to bring it to
                  completion. Computational results on a large testbed of mixed
                  integer linear programs from the literature are presented,
                  showing the potential of this approach even when embedded in
                  a proof-of-concept implementation. }
}

@Article{FisMonSal2012:or,
  author =       Fischetti #and# Monaci_M #and# {Domenico Salvagnin},
  title =        {Three Ideas for the Quadratic Assignment Problem},
  journal =      or,
  year =         2012,
  volume =       60,
  number =       4,
  pages =        {954--964}}

@Article{FisSal2009,
  title =        {Feasibility pump 2.0},
  author =       Fischetti #and# {Salvagnin, Domenico},
  journal =      mpc,
  volume =       1,
  number =       {2--3},
  pages =        {201--222},
  year =         2009,
  publisher =    springer
}

@Article{Fle1970bfgs,
  author       = {Fletcher, Roger},
  title        = {A new approach to variable metric algorithms},
  journal      = comjnl,
  year         = 1970,
  volume       = 13,
  number       = 3,
  pages        = {317--322},
  month        = sep,
  annote       = {One of the four papers that proposed BFGS.},
  doi          = {10.1093/comjnl/13.3.317},
  eprint       =
                  {https://academic.oup.com/comjnl/article-pdf/13/3/317/988678/130317.pdf},
  keywords     = {BFGS}
}

@Article{FleGlo99,
  title =        {Improved constructive multistart strategies for the
                  quadratic assignment problem using adaptive memory},
  author =       "Fleurent, Charles" #and# Glover_F,
  journal =      informsjoc,
  volume =       11,
  number =       2,
  pages =        {198--204},
  year =         1999,
}

@Article{Fli2007effects,
  title =        {The effects of adding objectives to an optimisation problem
                  on the solution set},
  author =       {Fliege, J{\"o}rg},
  journal =      orl,
  volume =       35,
  number =       6,
  pages =        {782--790},
  year =         2007,
  publisher =    {Elsevier}
}

@Article{FliVer1986,
  title =        {Distance Based Ranking Models},
  author =       {Fligner, Michael A. and Verducci, Joseph S.},
  journal =      rssb,
  number =       3,
  volume =       48,
  pages =        {359--369},
  year =         1986,
  keywords =     "Mallows model, ranking, probabilistic models",
  doi =          {10.1111/j.2517-6161.1986.tb01420.x},
}

@Article{Flo1956,
  author =       {M. M. Flood},
  title =        {The Travelling Salesman Problem},
  journal =      or,
  year =         1956,
  volume =       4,
  pages =        {61--75}
}

@Article{FloKel2010plos,
  author =       {Floreano, D. and Keller, L.},
  journal =      plosbio,
  pages =        {e1000292},
  title =        "Evolution of Adaptive Behaviour in Robots by Means
                  of {Darwinian} Selection",
  volume =       8,
  number =       1,
  year =         2010,
  doi =          {10.1371/journal.pbio.1000292},
  alias =        "Floreano:2010eg",
}

@Article{FloUrz2000nn,
  author =       {Floreano, D. and Urzelai, J.},
  title =        {Evolutionary robots with on-line self-organization
                  and behavioral fitness},
  journal =      nn,
  year =         2000,
  volume =       13,
  number =       {4-5},
  pages =        {431--443},
  alias =        {Floreano:2000uy},
}

@Article{Flores1986pragmatic,
  author       = {Flores, Benito E.},
  title        = {A pragmatic view of accuracy measurement in forecasting},
  journal      = omega,
  year         = 1986,
  volume       = 14,
  number       = 2,
  pages        = {93--98},
  annote       = {Proposed symmetric mean absolute percentage error (SMAPE)}
}

@Article{FocLodMil02tsptw,
  author =       Focacci #and# Lodi #and# Milano,
  title =        "A Hybrid Exact Algorithm for the {TSPTW}",
  journal =      informsjoc,
  year =         2002,
  volume =       14,
  pages =        {403--417},
  pdf =          {FocLodMil02Hybrid-TSPTW.pdf}
}

@Article{FonFle1995ec,
  title =        {An overview of evolutionary algorithms in multiobjective
                  optimization},
  author =       Fonseca #and# Fleming,
  journal =      ec,
  year =         1995,
  number =       1,
  pages =        {1--16},
  volume =       3,
  annote =       "Proposed FON benchmark problem"
}

@Article{FonFle1998:tsmca,
  author =       Fonseca #and# Fleming,
  title =        "Multiobjective Optimization and Multiple Constraint
                  Handling with Evolutionary Algorithms ({II}):
                  {Application} Example",
  journal =      tsmca,
  year =         1998,
  volume =       28,
  number =       1,
  pages =        {38--44},
  month =        jan,
  doi =          {10.1109/3468.650320}
}

@Article{FonFle1998:tsmca1,
  author =       Fonseca #and# Fleming,
  title =        "Multiobjective Optimization and Multiple Constraint
                  Handling with Evolutionary Algorithms ({I}): {A}
                  Unified Formulation",
  journal =      tsmca,
  year =         1998,
  volume =       28,
  number =       1,
  pages =        {26--37},
  month =        jan,
  doi =          {10.1109/3468.650319}
}

@Article{ForKea2009surrogate,
  author =       {Forrester, Alexander I. J. and Keane, Andy J.},
  title =        {Recent advances in surrogate-based optimization},
  journal =      {Progress in Aerospace Sciences},
  volume =       45,
  number =       {1-3},
  pages =        {50--79},
  doi =          {10.1016/j.paerosci.2008.11.001},
  year =         2009,
  keywords =     "Kriging; Gaussian Process; EGO; Design of Experiments"
}

@Article{FowGelKok2010ejor,
  title =        {Interactive evolutionary multi-objective optimization for
                  quasi-concave preference functions},
  journal =      ejor,
  volume =       206,
  number =       2,
  pages =        {417--425},
  year =         2010,
  doi =          {10.1016/j.ejor.2010.02.027},
  author =       {John W. Fowler} #and# {Esma S. Gel} #and# Koksalan #and#
                  Korhonen_P #and# {Jon L. Marquis} #and# Wallenius_J,
  keywords =     {Interactive optimization, Multi-objective optimization,
                  Evolutionary optimization, Knapsack problem},
  abstract =     {We present a new hybrid approach to interactive evolutionary
                  multi-objective optimization that uses a partial preference
                  order to act as the fitness function in a customized genetic
                  algorithm. We periodically send solutions to the decision
                  maker (DM) for her evaluation and use the resulting
                  preference information to form preference cones consisting of
                  inferior solutions. The cones allow us to implicitly rank
                  solutions that the DM has not considered. This technique
                  avoids assuming an exact form for the preference function,
                  but does assume that the preference function is
                  quasi-concave. This paper describes the genetic algorithm and
                  demonstrates its performance on the multi-objective knapsack
                  problem.}
}

@Article{Fox1993integrating,
  author =       Fox_BL,
  title =        {Integrating and accelerating tabu search, simulated
                  annealing, and genetic algorithms},
  journal =      aor,
  volume =       41,
  number =       2,
  pages =        {47--67},
  year =         1993,
  publisher =    springer
}

@Article{Fra2018tutorial,
  author       = {Peter I. Frazier},
  title        = {A Tutorial on {Bayesian} Optimization},
  journal      = arxiv # {1807.02811},
  year         = 2018,
  doi          = {10.48550/arXiv.1807.02811}
}

@Article{Fra2022:4or,
  title =        {Empirical Analysis of Stochastic Local Search Behaviour: Connecting Structure, Components and Landscape},
  author =       Franzin,
  journal =      j4or,
  year =         2022,
  doi =          {10.1007/s10288-022-00511-7}
}

@Article{FraBraBru2014automode,
  author =       "G. Francesca"#and#" M. Brambilla"#and#"
                  A. Brutschy"#and# Trianni_V #and# Birattari,
  title =        "{AutoMoDe}: A Novel Approach to the Automatic Design
                  of Control Software for Robot Swarms",
  journal =      swarm,
  year =         2014,
  volume =       8,
  number =       2,
  pages =        {89--112},
  doi =          {10.1007/s11721-014-0092-4},
}

@Article{FraBraBru2015automode,
  author =       {Francesca, Gianpiero and Brambilla, Manuele and Brutschy,
                  Arne and Garattoni, Lorenzo and Miletitch, Roman and
                  Podevijn, Gaetan and Reina, Andreagiovanni and Soleymani,
                  Touraj and Salvaro, Mattia and Pinciroli, Carlo and Mascia,
                  Franco} #and# Trianni_V #and# Birattari,
  title =        "{AutoMoDe-Chocolate}: Automatic Design of Control Software
                  for Robot Swarms",
  year =         2015,
  journal =      swarm,
  doi =          {10.1007/s11721-015-0107-9},
  keywords =     {Swarm robotics; Automatic design; AutoMoDe},
  language =     {English}
}

@Article{FraGupLei2004,
  title                    = {A Review and Classification of Heuristics for Permutation Flow-shop Scheduling with Makespan Objective},
  author                   = Framinan #and# {Jatinder N. D. Gupta} #and# Leisten,
  journal                  = jors,
  year                     = 2004,
  number                   = 12,
  pages                    = {1243--1255},
  volume                   = 55
}

@Article{FraPerStu2018ol,
  author =       Franzin #and# Perez_L #and# Stuetzle,
  title =        "Effect of Transformations of Numerical Parameters
                 in Automatic Algorithm Configuration",
  journal =      ol,
  year =         2018,
  volume =       12,
  number =       8,
  pages =        {1741--1753},
  doi =          {10.1007/s11590-018-1240-3}
}

@Article{FraSamDiC2016,
  author =       Franzin #and# {Sambo, Francesco and Di Camillo, Barbara},
  title =        {\rpackage{bnstruct}: an {R} package for {Bayesian} Network structure learning
                  in the presence of missing data},
  journal =      bioinfo,
  year =         2016,
  volume =       33,
  number =       8,
  pages =        {1250--1252}
}

@Article{FraStu2019:cor,
  author  = Franzin #and# Stuetzle,
  title   = "Revisiting Simulated Annealing: A Component-Based Analysis",
  journal = cor,
  volume  = 104,
  pages   = {191--206},
  year    = 2019,
  doi     = {10.1016/j.cor.2018.12.015},
}

@Article{FraStu2022:ejor,
  author =       Franzin #and# Stuetzle,
  title =        {A Landscape-based Analysis of Fixed Temperature and Simulated Annealing},
  year =         2023,
  journal =      ejor,
  volume =       304,
  number =       2,
  pages =        {395--410},
  doi =          {10.1016/j.ejor.2022.04.014}
}

@Article{FreDue2007clust,
  author =       {Brendan J. Frey and Delbert Dueck},
  title =        {Clustering by Passing Messages Between Data Points},
  year =         2007,
  month =        feb,
  publisher =    {American Association for the Advancement of Science ({AAAS})},
  volume =       315,
  number =       5814,
  pages =        {972--976},
  doi =          {10.1126/science.1136800},
  journal =      science,
  keywords =     "clustering; affinity propagation"
}

@Article{FreFleGui2015aggregation,
  title =        {Aggregation trees for visualization and dimension reduction
                  in many-objective optimization},
  author =       {de Freitas, Alan R. R.} #and# Fleming #and#
                  {Guimar{\~a}es, Frederico G.},
  journal =      is,
  volume =       298,
  pages =        {288--314},
  year =         2015,
  publisher =    {Elsevier}
}

@Article{FriChaMar2010:ijor,
  author =       Frikha #and# Chabchoub #and# Martel,
  title =        "Inferring criteria's relative importance coefficients
                  in {PROMETHEE II}",
  journal =      ijor,
  year =         2010,
  volume =       7,
  number =       2,
  pages =        {257--275},
}

@Article{FriJoh05FFTW,
  author  = {Matteo Frigo and Steven G. Johnson},
  title   = {The Design and Implementation of {FFTW3}},
  journal = prcieee,
  year    = 2005,
  volume  = 93,
  number  = 2,
  pages   = {216--231},
  note    = {Special issue on ``Program Generation, Optimization, and Platform Adaptation''}
}

@Article{Friedman1937use,
  title =        {The use of ranks to avoid the assumption of normality
                  implicit in the analysis of variance},
  author =       {Friedman, Milton},
  journal =      jasa,
  volume =       32,
  number =       200,
  pages =        {675--701},
  year =         1937,
}

@Article{FuEglLi2008,
 author  = {Z Fu and R Eglese and L Y O Li},
 title   = {A unified tabu search algorithm for vehicle routing problems with soft time windows},
 journal = jors,
 volume  = 59,
 number  = 5,
 pages   = {663--673},
 year    = 2008
}

@Article{FueDoeHar09ejor,
  author =       Fuellerer #and# Doerner #and# Hartl #and# Iori,
  title =        "Metaheuristics for vehicle routing problems with
                  three-dimensional loading constraints",
  journal =      ejor,
  year =         2009,
  volume =       201,
  number =       3,
  pages =        {751--759},
  doi =          "10.1016/j.ejor.2009.03.046",
}

@Article{FueDoeHarIor09,
  author =       Fuellerer #and# Doerner #and# Hartl #and# Iori,
  title =        {Ant colony optimization for the two-dimensional
                  loading vehicle routing problem},
  journal =      cor,
  year =         2009,
  volume =       36,
  number =       3,
  pages =        {655--673}
}

@Article{Fuk2008ec,
  title =        {Automated Discovery of Local Search Heuristics for
                  Satisfiability Testing},
  author =       Fukunaga,
  number =       1,
  journal =      ec,
  month =        mar,
  year =         2008,
  pages =        {31--61},
  volume =       16,
  doi =          {10.1162/evco.2008.16.1.31},
  abstract =     {The development of successful metaheuristic
                  algorithms such as local search for a difficult
                  problem such as satisfiability testing ({SAT)} is a
                  challenging task. We investigate an evolutionary
                  approach to automating the discovery of new local
                  search heuristics for {SAT}. We show that several
                  well-known {SAT} local search algorithms such as
                  Walksat and Novelty are composite heuristics that
                  are derived from novel combinations of a set of
                  building blocks. Based on this observation, we
                  developed {CLASS}, a genetic programming system that
                  uses a simple composition operator to automatically
                  discover {SAT} local search heuristics. New
                  heuristics discovered by {CLASS} are shown to be
                  competitive with the best Walksat variants,
                  including Novelty+. Evolutionary algorithms have
                  previously been applied to directly evolve a
                  solution for a particular {SAT} instance. We show
                  that the heuristics discovered by {CLASS} are also
                  competitive with these previous, direct evolutionary
                  approaches for {SAT}. We also analyze the local
                  search behavior of the learned heuristics using the
                  depth, mobility, and coverage metrics proposed by
                  Schuurmans and Southey.},
}

@Article{Fursin2011milepost,
  author    =  {Grigori Fursin and Yuriy Kashnikov and Abdul Wahid Memon and Zbigniew Chamski and Olivier Temam and Mircea Namolaru and Elad Yom-Tov and Bilha Mendelson and Ayal Zaks and Eric Courtois and Francois Bodin and Phil Barnard and Elton Ashton and Edwin Bonilla and John Thomson and  Christopher K. I. Williams and Michael O'Boyle},
  title     =  {Milepost {GCC}: Machine Learning Enabled Self-tuning Compiler},
  journal   =  ijpp,
  year      =  2011,
  volume    =  39,
  number    =  3,
  pages     =  {296--327},
  publisher =  springer-isorms,
  doi       =  {10.1007/s10766-010-0161-2}
}

@Article{GagPriGra02:jors,
  author =       Gagne_C #and# {W. L. Price and M. Gravel},
  title =        {Comparing an {ACO} algorithm with other heuristics
                  for the single machine scheduling problem with
                  sequence-dependent setup times},
  journal =      jors,
  year =         2002,
  volume =       53,
  pages =        {895--906}
}

@Article{GagSch2007,
  author =       Gagliolo #and# {J. Schmidhuber},
  title =        {Learning dynamic algorithm portfolios},
  journal =      amai,
  year =         2007,
  volume =       47,
  number =       {3-4},
  pages =        {295--328},
  doi =          "10.1007/s10472-006-9036-z",
  annote =       {fully dynamic and online algorithm selection technique, with
                  no separate training phase: all candidate algorithms are run
                  in parallel, while a model incrementally learns their runtime
                  distributions.}
}

@Article{GalHao1999heacol,
  title =        {Hybrid evolutionary algorithms for graph coloring},
  author =       {Galinier, Philippe} #and# Hao_JK,
  journal =      jco,
  volume =       3,
  number =       4,
  pages =        {379--397},
  year =         1999,
  publisher =    {Springer},
  doi = 	 {10.1023/A:1009823419804}
}

@Article{GalLeb1977ejor,
  title =        {Redundant objective functions in linear vector maximum
                  problems and their determination},
  journal =      ejor,
  volume =       1,
  number =       3,
  pages =        {176--184},
  year =         1977,
  doi =          {10.1016/0377-2217(77)90025-X},
  author =       {Tomas Gal and Heiner Leberling},
  abstract =     {Suppose that in a multicriteria linear programming problem
                  among the given objective functions there are some which can
                  be deleted without influencing the set E of all efficient
                  solutions. Such objectives are said to be
                  redundant. Introducing systems of objective functions which
                  realize their individual optimum in a single vertex of the
                  polyhedron generated by the restriction set, the notion of
                  relative or absolute redundant objectives is defined. A
                  theory which describes properties of absolute and relative
                  redundant objectives is developed. A method for determining
                  all the relative and absolute redundant objectives, based on
                  this theory, is given. Illustrative examples demonstrate the
                  procedure.}
}

@Article{GamDor00:informs,
  author =       Gambardella #and# Dorigo,
  title =        "Ant {Colony} {System} Hybridized with a New Local
                  Search for the Sequential Ordering Problem",
  volume =       12,
  number =       3,
  pages =        "237--255",
  journal =      informsjoc,
  year =         2000,
  anote =        "IJ.26",
}

@Article{GamMonWey12:ejor,
  author =       Gambardella #and# Montemanni_R #and# Weyland_D,
  title =        {Coupling Ant Colony Systems with Strong Local Searches},
  journal =      ejor,
  volume =       220,
  number =       3,
  year =         2012,
  pages =        {831--843},
  doi =          {10.1016/j.ejor.2012.02.038},
}

@Article{GanJasFre2000joh,
  author =       Gandibleux #and# Jaszkiewicz #and# Freville #and# Slowinski,
  title =        {Special Issue on {Multiple} {Objective} {Metaheuristics}},
  journal =      joh,
  year =         2000,
  volume =       6,
  number =       3,
  alias =        {Gandibleux00}
}

@Article{Gao2016,
  author =       {Gao, Kaizhou and Zhang, Yicheng and Sadollah, Ali and Su,
                  Rong},
  doi =          {10.1016/j.asoc.2016.07.029},
  journal =      asoco,
  keywords =     {harmony search algorithm,traffic light scheduling},
  month =        nov,
  pages =        {359--372},
  title =        {Optimizing urban traffic light scheduling problem using
                  harmony search with ensemble of local search},
  volume =       48,
  year =         2016
}

@Article{GaoNieLi2019visarxiv,
  title =        {Visualisation of {Pareto} Front Approximation: A Short Survey
                  and Empirical Comparisons},
  author =       {Gao, Huiru and Nie, Haifeng and Li, Ke},
  journal =      arxiv # {1903.01768},
  year =         2019,
  url =          {http://arxiv.org/abs/1903.01768}
}

@Article{GarAlbOli2012,
  author =       Garcia-Nieto #and# Alba_E #and# Olivera_AC,
  journal =      eaai,
  keywords =     {Cycle program optimization,Particle swarm
                  optimization,Realistic traffic instances,SUMO microscopic
                  simulator of urban mobility,Traffic light scheduling},
  month =        mar,
  number =       2,
  pages =        {274--283},
  title =        {Swarm intelligence for traffic light scheduling: Application
                  to real urban areas},
  volume =       25,
  year =         2012
}

@Article{GarCorHer07,
  author =       Garcia-Martinez #and# Cordon #and# Herrera_F,
  title =        {A taxonomy and an empirical analysis of multiple
                  objective ant colony optimization algorithms for the
                  bi-criteria {TSP}},
  journal =      ejor,
  volume =       180,
  number =       1,
  year =         2007,
  pages =        {116--148},
}

@Article{GarFer2015saferl,
  title =        {A comprehensive survey on safe reinforcement learning},
  author =       {Garc{\'i}a, Javier and Fern{\'a}ndez, Fernando},
  journal =      jmlr,
  volume =       16,
  number =       1,
  pages =        {1437--1480},
  year =         2015,
  epub = {http://jmlr.org/papers/volume16/garcia15a/garcia15a.pdf}
}

@Article{GarFerLueHer2010tests,
  title =        {Advanced nonparametric tests for multiple comparisons in the
                  design of experiments in computational intelligence and data
                  mining: Experimental analysis of power},
  author =       {Garc{\'i}a, Salvador and Fern{\'a}ndez, Alberto and Luengo,
                  Juli{\'a}n} #and# Herrera_F,
  journal =      is,
  volume =       180,
  number =       10,
  pages =        {2044--2064},
  year =         2010,
}

@Article{GarGloRodLozMar2014,
  author =       Garcia-Martinez #and# Glover_F #and# Rodriguez_F #and# Lozano_M #and# Marti_R,
  title =        {Strategic Oscillation for the Quadratic Multiple Knapsack Problem},
  journal =      coa,
  year =         2014,
  volume =       58,
  number =       1,
  pages =        {161--185}
}

@Article{GarJohSet76,
  author =       {M. R. Garey} #and# Johnson_DS #and#{R. Sethi},
  title =        {The Complexity of Flowshop and Jobshop Scheduling},
  journal =      mor,
  year =         1976,
  volume =       1,
  pages =        {117--129}
}

@Article{GarKal2001eff,
  author =       {Garnier, Josselin and Kallel, Leila},
  title =        {Efficiency of Local Search with Multiple Local Optima},
  journal =      siamdm,
  volume =       15,
  number =       1,
  pages =        {122--141},
  year =         2001,
  doi =          {10.1137/S0895480199355225}
}

@Article{GarMolLoz2009joh,
  author =       {Garc{\'i}a, Salvador} #and# Molina_D #and# Lozano_M #and#
                  Herrera_F,
  journal =      joh,
  number =       617,
  title =        {A study on the use of non-parametric tests for analyzing the
                  evolutionary algorithms' behaviour: a case study on the
                  {CEC'2005} {Special} {Session} on {Real} {Parameter}
                  {Optimization}},
  volume =       15,
  year =         2009,
  pages =        {617--644},
  doi =          "10.1007/s10732-008-9080-4"
}

@Article{GarOliAlb2013tec,
  author =       Garcia-Nieto #and# Olivera_AC #and# Alba_E,
  doi =          {10.1109/TEVC.2013.2260755},
  journal =      tec,
  month =        dec,
  number =       6,
  pages =        {823--839},
  title =        {Optimal Cycle Program of Traffic Lights With Particle Swarm
                  Optimization},
  volume =       17,
  year =         2013
}

@Article{GarRodLoz2012soco,
  author =       Garcia-Martinez #and# Rodriguez_F #and# Lozano_M,
  title =        {Arbitrary function optimisation with metaheuristics: No free
                  lunch and real-world problems},
  journal =      soco,
  year =         2012,
  volume =       16,
  number =       12,
  pages =        {2115--2133},
  doi =          {10.1007/s00500-012-0881-x},
}

@Article{GarRodLoz2014,
  author =       Garcia-Martinez #and# Rodriguez_F #and# Lozano_M,
  title =        {Tabu-enhanced Iterated Greedy Algorithm: A Case Study in the Quadratic Multiple Knapsack Problem},
  journal =      ejor,
  year =         2014,
  volume =       232,
  number =       3,
  pages =        {454--463}
}

@Article{GauDodGro2012gsa,
  author =       {Melvin, Gauci} #and# {Tony J. Dodd} #and# Gross_R,
  title =        {Why `{GSA}: a gravitational search algorithm' is not
                  genuinely based on the law of gravity},
  journal =      natcomp,
  year =         2012,
  volume =       11,
  number =       4,
  pages =        {719--720},
}

@Article{Gei2011,
  author    = {Martin Josef Geiger},
  title     = {Decision Support for Multi-objective Flow Shop Scheduling
               by the {Pareto} Iterated Local Search Methodology},
  journal   = cie,
  volume    = 61,
  number    = 3,
  pages     = {805--812},
  year      = 2011,
}

@Article{Geiger2017:ejor,
  author = 	 {Martin Josef Geiger},
  title = 	 {A Multi-threaded Local Search Algorithm and Computer Implementation for the Multi-mode, Resource-constrained Multi-project Scheduling Problem},
  journal = 	 ejor,
  year = 	 2017,
  volume = 	 256,
  pages = 	 {729--741}
}

@Article{GemGem1984,
  author =       Geman_S #and# Geman_D,
  title =        {Stochastic Relaxation, {Gibbs} Distributions, and the {Bayesian} Restoration of Images},
  journal =      tpami,
  volume =       6,
  number =       6,
  pages =        {721--741},
  year =         1984,
  publisher =    ieeep
}

@Article{GenGuePot1999ts,
  title   = {Parallel tabu search for real-time vehicle routing and dispatching},
  author  = Gendreau #and# {Francois Guertin} #and# Potvin #and# Taillard,
  journal = ts,
  volume  = 33,
  number  = 4,
  pages   = {381--390},
  year    = 1999
}

@Article{GenGuePot2006trc,
  title   = {Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick-ups and deliveries},
  author  = Gendreau #and# {Francois Guertin} #and# Potvin #and# {S{\'e}guin, Ren{\'e}},
  journal = trc,
  volume  = 14,
  number  = 3,
  pages   = {157--174},
  year    = 2006,
  publisher={Elsevier}
}

@Article{GenMit2014,
  title =        {Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey},
  author =       {Gen, Mitsuo and Lin, Lin},
  journal =      jima,
  volume =       25,
  number =       5,
  pages =        {849--866},
  year =         2014,
  publisher =    springer
}

@Article{GenPogTul2010,
  title =        {Variable selection using random forests},
  author =       {Genuer, Robin and Poggi, Jean-Michel and Tuleau-Malot, Christine},
  journal =      prl,
  volume =       31,
  number =       14,
  pages =        {2225--2236},
  year =         2010,
  publisher =    elsevier
}

@Article{Gendreau98tsptw,
  author =       Gendreau #and# Hertz_A #and# Laporte #and# Stan_M,
  title =        {A Generalized Insertion Heuristic for the Traveling
                  Salesman Problem with Time Windows},
  journal =      or,
  year =         1998,
  volume =       46,
  pages =        {330--335}
}

@Article{GenGhiGue2015tdtsp,
  author       = Gendreau #and# {Ghiani, Gianpaolo and Guerriero,
                  Emanuela},
  title        = {Time-dependent routing problems: A review},
  journal      = cor,
  year         = 2015,
  volume       = 64,
  pages        = {189--197},
  month        = dec,
  doi          = {10.1016/j.cor.2015.06.001},
}

@Article{GerHorTen2015comp,
  author =       {Gershman, Samuel J. and Horvitz, Eric J. and Tenenbaum,
                  Joshua B.},
  title =        {Computational rationality: A converging paradigm for
                  intelligence in brains, minds, and machines},
  volume =       349,
  number =       6245,
  pages =        {273--278},
  year =         2015,
  doi =          {10.1126/science.aac6076},
  publisher =    {American Association for the Advancement of Science},
  abstract =     {After growing up together, and mostly growing apart in the
                  second half of the 20th century, the fields of artificial
                  intelligence (AI), cognitive science, and neuroscience are
                  reconverging on a shared view of the computational
                  foundations of intelligence that promotes valuable
                  cross-disciplinary exchanges on questions, methods, and
                  results. We chart advances over the past several decades that
                  address challenges of perception and action under uncertainty
                  through the lens of computation. Advances include the
                  development of representations and inferential procedures for
                  large-scale probabilistic inference and machinery for
                  enabling reflection and decisions about tradeoffs in effort,
                  precision, and timeliness of computations. These tools are
                  deployed toward the goal of computational rationality:
                  identifying decisions with highest expected utility, while
                  taking into consideration the costs of computation in complex
                  real-world problems in which most relevant calculations can
                  only be approximated. We highlight key concepts with examples
                  that show the potential for interchange between computer
                  science, cognitive science, and neuroscience.},
  epub =       {https://science.sciencemag.org/content/349/6245/273.full.pdf},
  journal =      science
}

@Article{GeuDamWeh2006extratrees,
  author =       {Pierre Geurts and Damien Ernst and Louis Wehenkel},
  title =        {Extremely randomized trees},
  doi =          {10.1007/s10994-006-6226-1},
  year =         2006,
  month =        mar,
  publisher =    {Springer Science and Business Media {LLC}},
  volume =       63,
  number =       1,
  pages =        {3--42},
  journal =      ml,
  annote =       "Proposed ExtraTrees"
}

@Article{GheGobLeu2003gfs,
  author =       {Ghemawat, Sanjay and Gobioff, Howard and Leung, Shun-Tak},
  title =        {The {Google} {File} {System}},
  journal =      {SIGOPS Oper. Syst. Rev.},
  volume =       37,
  number =       5,
  year =         2003,
  month =        dec,
  pages =        {29--43},
  publisher =    acm-pub,
  address =      add-ny,
}

@Article{GhoNad09:asc,
  author =       Ghoseiri #and# Nadjari,
  title =        {An ant colony optimization algorithm for the bi-objective
                  shortest path problem},
  journal =      asoco,
  volume =       10,
  number =       4,
  year =         2010,
  pages =        {1237--1246}
}

@Article{GirRabPib2016,
  title =        {Quantification of Treatment Effect Modification on Both an Additive and Multiplicative Scale},
  author =       {Girerd, Nicolas and Rabilloud, Muriel and Pibarot, Philippe
                  and Mathieu, Patrick and Roy, Pascal},
  journal =      pone,
  year =         2016,
  month =        apr,
  volume =       11,
  pages =        {1--14},
  number =       4,
  doi =          {10.1371/journal.pone.0153010}
}

@Article{Glo1977,
  author =       Glover_F,
  title =        {Heuristics for Integer Programming Using Surrogate Constraints},
  journal =      ds,
  year =         1977,
  volume =       8,
  pages =        {156--166}
}

@Article{Glo1986,
  author =       Glover_F,
  title =        {Future Paths for Integer Programming and Links to
                  Artificial Intelligence},
  journal =      cor,
  year =         1986,
  volume =       13,
  number =       5,
  pages =        {533--549}
}

@Article{Glo1989,
  title =        {Tabu Search -- {Part} {I}},
  author =       Glover_F,
  journal =      informsjoc,
  volume =       1,
  number =       3,
  pages =        {190--206},
  year =         1989,
  doi = {10.1287/ijoc.1.3.190}
}

@Article{Glo1990,
  author =       Glover_F,
  title =        "Tabu Search -- {Part} {II}",
  journal =      informsjoc,
  year =         1990,
  number =       1,
  volume =       2,
  pages =        "4--32",
}

@Article{GloHao2011so,
  author =       Glover_F #and# Hao_JK,
  title =        {The case for Strategic Oscillation},
  journal =      aor,
  year =         2011,
  volume =       183,
  number =       1,
  pages =        {163--173},
}

@Article{GloKocAli98,
  author =       Glover_F #and# Kochenberger #and# {Bahram Alidaee},
  title =        {Adaptive Memory Tabu Search for Binary Quadratic Programs},
  journal =      ms,
  year =         1998,
  volume =       44,
  number =       3,
  pages =        {336--345}
}

@Article{GloLuHao2010diversif,
  title =        {Diversification-driven tabu search for unconstrained binary
                  quadratic problems},
  author =       Glover_F #and# {L{\"u}, Zhipeng} #and# Hao_JK,
  journal =      j4or,
  volume =       8,
  number =       3,
  pages =        {239--253},
  year =         2010,
  doi =          {10.1007/s10288-009-0115-y}
}

@Article{GoeScho2014recovery,
  title =        {Recovery-to-optimality: A new two-stage approach to
                  robustness with an application to aperiodic timetabling},
  author =       {Goerigk, Marc} #and# Schobel_A,
  journal =      cor,
  volume =       52,
  pages =        {1--15},
  year =         2014,
  publisher =    {Elsevier}
}

@Article{Gol1970bfgs,
  author       = {Donald Goldfarb},
  title        = {A Family of Variable-Metric Methods Derived by Variational
                  Means},
  journal      = mathcom,
  year         = 1970,
  volume       = 24,
  number       = 109,
  pages        = {23--26},
  annote       = {One of the four papers that proposed BFGS.},
  publisher    = ams-pub,
  eprint       = {http://www.jstor.org/stable/2004873},
  keywords     = {BFGS}
}
@Article{Gold1990pm,
  title =        {Probability matching, the magnitude of reinforcement, and
                  classifier system bidding},
  author =       Goldberg,
  journal =      ml,
  volume =       5,
  number =       4,
  pages =        {407--425},
  year =         1990,
}

@Article{GonZhaChi2018kbs,
  title =        {The optimization ordering model for intuitionistic fuzzy
                  preference relations with utility functions},
  journal =      kbs,
  volume =       162,
  pages =        {174--184},
  year =         2018,
  annote =       {Special Issue on intelligent decision-making and consensus
                  under uncertainty in inconsistent and dynamic environments},
  issn =         {0950-7051},
  doi =          {10.1016/j.knosys.2018.07.012},
  author =       {Zaiwu Gong and Ning Zhang and Francisco Chiclana},
  keywords =     {Intuitionistic fuzzy preference relation, Utility function,
                  Ranking, Multiplicative consistency, Non-archimedean
                  infinitesimal},
  abstract =     {Intuitionistic fuzzy sets describe information from the three
                  aspects of membership degree, non-membership degree and
                  hesitation degree, which has more practical significance when
                  uncertainty pervades qualitative decision problems. In this
                  paper, we investigate the problem of ranking intuitionistic
                  fuzzy preference relations (IFPRs) based on various
                  non-linear utility functions. First, we transform IFPRs into
                  their isomorphic interval-value fuzzy preference relations
                  (IVFPRs), and utilise non-linear utility functions, such as
                  parabolic, S-shaped, and hyperbolic absolute risk aversion,
                  to fit the true value of a decision-maker's
                  judgement. Ultimately, the optimization ordering models for
                  the membership and non-membership of IVFPRs based on utility
                  function are constructed, with objective function aiming at
                  minimizing the distance deviation between the multiplicative
                  consistency ideal judgment and the actual judgment,
                  represented by utility function, subject to the
                  decision-maker's utility constraints. The proposed models
                  ensure that more factual and optimal ranking of alternative
                  is acquired, avoiding information distortion caused by the
                  operations of intervals. Second, by introducing a
                  non-Archimedean infinitesimal, we establish the optimization
                  ordering model for IFPRs with the priority of utility or
                  deviation, which realises the goal of prioritising solutions
                  under multi-objective programming. Subsequently, we verify
                  that a close connection exists between the ranking for
                  membership and non-membership degree IVFPRs. Comparison
                  analyses with existing approaches are summarized to
                  demonstrate that the proposed models have advantage in
                  dealing with group decision making problems with IFPRs.}
}

@Article{GorKlaRuz2011connectedness,
  author =       {Gorski, Jochen} #and# Klamroth #and# {Ruzika, Stefan},
  journal =      jota,
  number =       3,
  pages =        {475--497},
  publisher =    {Springer},
  title =        {Connectedness of Efficient Solutions in Multiple Objective
                  Combinatorial Optimization},
  volume =       150,
  year =         2011,
  doi =          {10.1007/s10957-011-9849-8}
}

@Article{Gos2009rl,
  author =       {Gosavi, Abhijit},
  title =        {Reinforcement Learning: A Tutorial Survey and Recent
                  Advances},
  journal =      informsjoc,
  volume =       21,
  number =       2,
  pages =        {178--192},
  year =         2009,
  doi =          {10.1287/ijoc.1080.0305},
}

@Article{GouOrbToi03,
  author       = {N. I. M. Gould and D. Orban and P. L. Toint},
  title        = {{CUTEr} and {SifDec}: A constrained and unconstrained testing
                  environment, revisited},
  journal      = toms,
  year         = 2003,
  volume       = 29,
  pages        = {373--394}
}

@Article{GraChi1996jair,
  author       = {Jonathan Gratch and Steve A. Chien},
  title        = {Adaptive Problem-solving for Large-scale Scheduling Problems:
                  A Case Study},
  journal      = jair,
  year         = 1996,
  volume       = 4,
  pages        = {365--396},
  annote       = {Earliest hyper-heuristic?}
}

@Article{GraHer2009treedgp,
  author =       {Robert B. Gramacy and Lee, Herbert K. H.},
  title =        {Bayesian Treed {Gaussian} Process Models With an Application
                  to Computer Modeling},
  journal =      jasa,
  volume =       103,
  pages =        {1119--1130},
  year =         2008,
  doi =          "10.1198/016214508000000689",
  keywords =     "Treed-GP"
}

@Article{GraJuaLou2016,
  author =       {Alex Grasas and Angel A. Juan} #and# Ramalhinho_Lourenco,
  title =        {{SimILS}: A Simulation-based Extension of the Iterated Local Search Metaheuristic for Stochastic Combinatorial Optimization},
  journal =      js,
  year =         2016,
  volume =       10,
  number =       1,
  pages =        {69--77}
}

@Article{GraPriGag02,
  author =       {M. Gravel and W. L. Price} #and# Gagne_C,
  title =        {Scheduling continuous casting of aluminum using a
                  multiple objective ant colony optimization
                  metaheuristic},
  journal =      ejor,
  year =         2002,
  volume =       143,
  number =       1,
  pages =        {218--229},
  doi =          {10.1016/S0377-2217(01)00329-0},
  alias =        {GraPriGag:ejor},
}

@Article{Gre86,
  author       = {John J. Grefenstette},
  title        = {Optimization of Control Parameters for Genetic Algorithms},
  journal      = tsmc,
  year         = 1986,
  volume       = 16,
  number       = 1,
  pages        = {122--128},
  keywords     = {parameter tuning},
  doi          = {10.1109/TSMC.1986.289288}
}

@Article{GreKadMouSlo2011:ejor,
  author =       Greco #and# Kadzinski #and# Mousseau #and# Slowinski,
  title =        "{ELECTRE}$^\mathrm{{GKMS}}$: Robust ordinal regression for outranking methods",
  journal =      ejor,
  volume =       214,
  number =       1,
  pages =        "118--135",
  year =         2011,
}

@Article{GreMouSlo2014ejor,
  author    = Greco #and# Mousseau #and# Slowinski,
  title     = {Robust ordinal regression for value functions handling interacting
               criteria},
  journal   = ejor,
  volume    = 239,
  number    = 3,
  pages     = {711--730},
  year      = 2014,
  doi       = {10.1016/j.ejor.2014.05.022}
}

@Article{GriBauIoa2018modeling,
  title =        {Modelling science trustworthiness under publish or perish pressure},
  author =       {David R. Grimes and Chris T. Bauch}  #and# Ioannidis,
  journal =      {Royal Society Open Science},
  volume =       5,
  pages =        {171511},
  year =         2018,
}

@Article{GroDelTad2004,
  author =       Grosso #and# DellaCroce #and# {R. Tadei},
  title =        {An Enhanced Dynasearch Neighborhood for the
                  Single-Machine Total Weighted Tardiness Scheduling
                  Problem},
  journal =      orl,
  year =         2004,
  volume =       32,
  number =       1,
  pages =        {68--72}
}

@Article{GroJamLoc2009,
  author =       Grosso #and# {A. R. M. J. U. Jamali and Marco Locatelli},
  title =        {Finding Maximin Latin Hypercube Designs by Iterated Local Search Heuristics},
  journal =      ejor,
  year =         2009,
  volume =       197,
  number =       2,
  pages =        {541--547}
}

@Article{GroKayKnoVan2013,
  title =        {The "big data" revolution in healthcare},
  author =       {Groves, Peter and Kayyali, Basel and Knott, David and Van
                  Kuiken, Steve},
  journal =      {McKinsey Quarterly},
  volume =       2,
  year =         2013
}

@Article{GroMan2019hvsubset,
  title =        {Hypervolume subset selection with small subsets},
  author =       {Groz, Beno{\^i}t and Maniu, Silviu},
  journal =      ec,
  year =         2019,
  number =       4,
  pages =        {611--637},
  volume =       27,
}

@Article{GruFon2002spl,
  author       = Grunert_da_Fonseca #and# Fonseca,
  title        = {A link between the multivariate cumulative distribution
                  function and the hitting function for random closed sets},
  journal      = spl,
  year         = 2002,
  volume       = 57,
  number       = 2,
  pages        = {179--182},
  alias        = {Fonseca02a},
  doi          = {10.1016/S0167-7152(02)00046-9}
}

@Article{GueFonPaq2021hv,
  title                    = {The Hypervolume Indicator: Computational Problems and Algorithms},
  author                   = Guerreiro_A #and# Fonseca #and# Paquete,
  journal                  = acm-cs,
  year                     = 2021,
  number                   = 6,
  pages                    = {1--42},
  volume                   = 54,
}

@Article{GueManFig2021exacthv,
  author =       Guerreiro_A #and# {Vasco Manquinho} #and# Figueira,
  title =        {Exact hypervolume subset selection through incremental
                  computations},
  doi =          {10.1016/j.cor.2021.105471},
  year =         2021,
  month =        dec,
  volume =       136,
  pages =        {105--471},
  journal =      cor,
}

@Article{Gui2011objred,
  author =       {Gonzalo Guill{\'e}n-Gos{\'a}lbez},
  title =        {A novel {MILP}-based objective reduction method for
                  multi-objective optimization: Application to environmental
                  problems},
  journal =      compchemeng,
  volume =       35,
  number =       8,
  pages =        {1469--1477},
  year =         2011,
  issn =         {0098-1354},
  doi =          {10.1016/j.compchemeng.2011.02.001},
  keywords =     {Environmental engineering, Life cycle assessment,
                  Multi-objective optimization, Objective reduction},
  abstract =     {Multi-objective optimization has recently emerged as a useful
                  technique in sustainability analysis, as it can assist in the
                  study of optimal trade-off solutions that balance several
                  criteria. The main limitation of multi-objective optimization
                  is that its computational burden grows in size with the
                  number of objectives. This computational barrier is critical
                  in environmental applications in which decision-makers seek
                  to minimize simultaneously several environmental indicators
                  of concern. With the aim to overcome this limitation, this
                  paper introduces a systematic method for reducing the number
                  of objectives in multi-objective optimization with emphasis
                  on environmental problems. The approach presented relies on a
                  novel mixed-integer linear programming formulation that
                  minimizes the error of omitting objectives. We test the
                  capabilities of this technique through two environmental
                  problems of different nature in which we attempt to minimize
                  a set of life cycle assessment impacts. Numerical examples
                  demonstrate that certain environmental metrics tend to behave
                  in a non-conflicting manner, which makes it possible to
                  reduce the dimension of the problem without losing
                  information.}
}

@Article{GunGilAha2018repro,
  author =       {Odd Erik Gundersen and Yolanda Gil and David W. Aha},
  title =        {On Reproducible {AI}: Towards Reproducible Research, Open
                  Science, and Digital Scholarship in {AI} Publications},
  doi =          {10.1609/aimag.v39i3.2816},
  year =         2018,
  month =        sep,
  publisher =    {Association for the Advancement of Artificial Intelligence
                  ({AAAI})},
  volume =       39,
  number =       3,
  pages =        {56--68},
  journal =      aim,
  annote =       "The reproducibility guidelines can be found here:
                  \url{https://folk.idi.ntnu.no/odderik/reproducibility_guidelines.pdf}
                  and a short how-to can be found here:
                  \url{https://folk.idi.ntnu.no/odderik/reproducibility_guidelines_how_to.html}"
}

@Article{GunNgPoh2012,
  title =        {A Hybridized {Lagrangian} Relaxation and Simulated Annealing
                  Method for the Course Timetabling Problem},
  author =       Gunawan_A #and# Ng_KM #and# Poh_KL,
  journal =      cor,
  volume =       39,
  number =       12,
  pages =        {3074--3088},
  year =         2012,
  publisher =    elsevier
}

@Article{Gup1986,
  title   =    {Flowshop schedules with sequence dependent setup times},
  author  =    {J. N. D. Gupta},
  journal =    {Journal of Operations Research Society of Japan},
  volume  =    29,
  year    =    1986,
  pages   =    {206--219}
}

@Article{Gut2000:fgcs,
  author =       Gutjahr,
  title =        "A {Graph}-based {Ant} {System} and its Convergence",
  journal =      fgcs,
  year =         2000,
  volume =       16,
  number =       8,
  pages =        "873--888",
}

@Article{Gut2002:ipl,
  author =       Gutjahr,
  title =        "{ACO} Algorithms with Guaranteed Convergence to the
                  Optimal Solution",
  journal =      ipl,
  year =         2002,
  volume =       82,
  number =       3,
  pages =        "145--153"
}

@Article{Gut2006:mcap,
  author =       Gutjahr,
  title =        {On the finite-time dynamics of ant colony
                  optimization},
  journal =      mcap,
  year =         2006,
  volume =       8,
  number =       1,
  pages =        {105--133}
}

@Article{Gut2007:swarm,
  author =       Gutjahr,
  title =        "Mathematical runtime analysis of {ACO} algorithms:
                  survey on an emerging issue",
  journal =      swarm,
  volume =       1,
  number =       1,
  year =         2007,
  pages =        {59--79},
}

@Article{Gut2007cor,
  title =        "An {ACO} algorithm for a dynamic regional nurse-scheduling
                  problem in {Austria} ",
  journal =      cor,
  volume =       34,
  number =       3,
  pages =        "642--666",
  year =         2007,
  anote =        "Logistics of Health Care Management Part Special Issue:
                  Logistics of Health Care Management ",
  doi =          "10.1016/j.cor.2005.03.018",
  author =       Gutjahr #and# " Marion S. Rauner",
  abstract =     "To the best of our knowledge, this paper describes the first
                  ant colony optimization (ACO) approach applied to nurse
                  scheduling, analyzing a dynamic regional problem which is
                  currently under discussion at the Vienna hospital
                  compound. Each day, pool nurses have to be assigned for the
                  following days to public hospitals while taking into account
                  a variety of soft and hard constraints regarding working date
                  and time, working patterns, nurses qualifications, nurses
                  and hospitals preferences, as well as costs. Extensive
                  computational experiments based on a four week simulation
                  period were used to evaluate three different scenarios
                  varying the number of nurses and hospitals for six different
                  hospitals demand intensities. The results of our simulations
                  and optimizations reveal that the proposed {ACO} algorithm
                  achieves highly significant improvements compared to a greedy
                  assignment algorithm."
}

@Article{Gut2008:cor,
  author    = Gutjahr,
  title     = {First steps to the runtime complexity analysis of ant colony
               optimization},
  journal   = cor,
  volume    = 35,
  number    = 9,
  year      = 2008,
  pages     = {2711--2727},
}

@Article{GutSeb2008,
  author =       Gutjahr #and# {G. Sebastiani},
  title =        {Runtime analysis of ant colony optimization with best-so-far
                  reinforcement},
  journal =      mcap,
  year =         2008,
  volume =       10,
  number =       3,
  pages =        {409--433}
}

@Article{GutYeoZve2002,
  author =       {Gutin, Gregory and Yeo, Anders and Zverovich, Alexey},
  title =        {Traveling salesman should not be greedy: domination analysis
                  of greedy-type heuristics for the {TSP}},
  journal =      dam,
  volume =       117,
  number =       {1--3},
  year =         2002
}

@Article{GuyWesBar2002rfe,
  title =        {Gene selection for cancer classification using support vector
                  machines},
  author =       {Guyon, Isabelle and Weston, Jason and Barnhill, Stephen and
                  Vapnik, Vladimir},
  journal =      ml,
  volume =       46,
  number =       1,
  pages =        {389--422},
  year =         2002,
  publisher =    {Springer},
  keywords =     "recursive feature elimination"
}

@Article{HaaSakTam2001,
  title =       {An adaptive {Metropolis} algorithm},
  author =      {Haario, Heikki and Saksman, Eero and Tamminen, Johanna},
  journal =     bernoulli,
  volume =      7,
  number =      2,
  pages =       {223--242},
  year =        2001
}

@Article{HadRee2013borg,
  author =       Hadka_D #and# Reed_P,
  title =        {Borg: An Auto-Adaptive Many-Objective Evolutionary Computing
                  Framework},
  journal =      ec,
  number =       2,
  pages =        {231--259},
  volume =       21,
  year =         2013,
  alias =        "Hadka13borg",
}

@Article{HadReed2012ec,
  author =       Hadka_D #and# Reed_P,
  title =        "Diagnostic Assessment of Search Controls and Failure Modes in
                  Many-Objective Evolutionary Optimization",
  journal =      ec,
  volume =       20,
  number =       3,
  year =         2012,
  pages =        {423--452}
}

@Article{HadRus1969rules,
  title =        {Rules for ordering uncertain prospects},
  author =       {Hadar, Josef and Russell, William R.},
  journal =      amecrev,
  volume =       59,
  number =       1,
  pages =        {25--34},
  year =         1969,
  epub =         {https://www.jstor.org/stable/1811090},
  keywords =     "stochastic dominance"
}

@Article{HaiLasWis1971bicriterion,
  title =        {On a bicriterion formation of the problems of integrated
                  system identification and system optimization},
  author =       {Haimes, Y. and Lasdon, L. and Da Wismer, D.},
  journal =      tsmc,
  volume =       1,
  number =       3,
  pages =        {296--297},
  year =         1971,
  doi =          "10.1109/TSMC.1971.4308298",
  keywords =     "epsilon-constraint method"
}

@Article{HajLin1992decomposition,
  title =        {Genetic search strategies in multicriterion optimal design},
  author =       {Hajela, Prabhat and Lin, C-Y},
  journal =      structopt,
  volume =       4,
  number =       2,
  pages =        {99--107},
  year =         1992,
  publisher =    {Springer}
}

@Article{HajSas1989,
  title =        {Simulated annealing--to cool or not},
  author =       Hajek_B #and# Sasaki_G,
  journal =      scl,
  volume =       12,
  number =       5,
  pages =        {443--447},
  year =         1989,
  publisher =    elsevier
}

@Article{Hajek1988,
  title =        {Cooling Schedules for Optimal Annealing},
  author =       Hajek_B,
  journal =      mor,
  volume =       13,
  number =       2,
  pages =        {311--329},
  year =         1988,
  publisher =    informs,
}

@Article{HalOliSud2022ai,
  author =       Hall_GT #and# Oliveto_PS #and# Sudholt,
  title =        {On the impact of the performance metric on efficient
                  algorithm configuration},
  doi =          {10.1016/j.artint.2021.103629},
  year =         2022,
  month =        feb,
  publisher =    {Elsevier {BV}},
  volume =       303,
  pages =        103629,
  journal =      aij,
  keywords =     "irace",
}

@Article{HamLah2016path,
  title =        {Path dependence in {Operational} {Research}--{How} the
                  modeling process can influence the results},
  author =       Hamalainen #and# {Lahtinen, Tuomas J.},
  doi =          {10.1016/j.orp.2016.03.001},
  abstract =     {In Operational Research practice there are almost always
                  alternative paths that can be followed in the modeling and
                  problem solving process. Path dependence refers to the impact
                  of the path on the outcome of the process. The steps of the
                  path include, e.g. forming the problem solving team, the
                  framing and structuring of the problem, the choice of model,
                  the order in which the different parts of the model are
                  specified and solved, and the way in which data or
                  preferences are collected. We identify and discuss seven
                  possibly interacting origins or drivers of path dependence:
                  systemic origins, learning, procedure, behavior, motivation,
                  uncertainty, and external environment. We provide several
                  ideas on how to cope with path dependence.},
  journal =      orp,
  month =        jan,
  volume =       3,
  year =         2016,
  keywords =     {Behavioral Biases, Behavioral Operational Research, Ethics in
                  modelling, OR practice, Path dependence, Systems perspective},
  pages =        {14--20}
}

@Article{HamLuoSaa2013bor,
  author =       Hamalainen #and# {Luoma, Jukka and Saarinen, Esa},
  title =        {On the importance of behavioral operational research: {The}
                  case of understanding and communicating about dynamic
                  systems},
  volume =       228,
  shorttitle =   {On the importance of behavioral operational research},
  doi =          {10.1016/j.ejor.2013.02.001},
  abstract =     {We point out the need for Behavioral Operational Research
                  (BOR) in advancing the practice of OR. So far, in OR
                  behavioral phenomena have been acknowledged only in
                  behavioral decision theory but behavioral issues are always
                  present when supporting human problem solving by
                  modeling. Behavioral effects can relate to the group
                  interaction and communication when facilitating with OR
                  models as well as to the possibility of procedural mistakes
                  and cognitive biases. As an illustrative example we use well
                  known system dynamics studies related to the understanding of
                  accumulation. We show that one gets completely opposite
                  results depending on the way the phenomenon is described and
                  how the questions are phrased and graphs used. The results
                  suggest that OR processes are highly sensitive to various
                  behavioral effects. As a result, we need to pay attention to
                  the way we communicate about models as they are being
                  increasingly used in addressing important problems like
                  climate change.},
  number =       3,
  journal =      ejor,
  month =        aug,
  year =         2013,
  pages =        {623--634}
}

@Article{HamRuh1994,
  author =       {Hamacher, Horst W. and Ruhe, G\"{u}nter},
  title =        {On spanning tree problems with multiple objectives},
  journal =      aor,
  year =         1994,
  volume =       52,
  number =       4,
  pages =        {209--230},
}

@Article{HanAugBroTus2022anytime,
  author       = Hansen_N #and# Auger_A #and# Brockhoff#and# Tusar,
  title        = {Anytime Performance Assessment in Blackbox Optimization
                  Benchmarking},
  journal      = tec,
  year         = 2022,
  volume       = 26,
  number       = 6,
  pages        = {1293--1305},
  month        = dec,
  doi          = {10.1109/TEVC.2022.3210897}
}

@Article{HanAugMer2016coco,
  title =        {{COCO}: A platform for comparing continuous optimizers in a
                  black-box setting},
  author =       Hansen_N #and# Auger_A #and# {Mersmann, Olaf} #and# Tusar #and# Brockhoff,
  journal =      arxiv # {1603.08785},
  year =         2016,
  note = "Published as \cite{HanAugMer2020coco}"
}

@Article{HanAugMer2020coco,
  title =        {{COCO}: A platform for comparing continuous optimizers in a
                  black-box setting},
  author =       Hansen_N #and# Auger_A #and# "Ros, Raymond" #and# {Mersmann,
                  Olaf} #and# Tusar #and# Brockhoff,
  journal =      oms,
  pages =        {1--31},
  year =         2020,
  volume =       36,
  number =       1,
  doi =          "10.1080/10556788.2020.1808977",
  publisher =    {Taylor \& Francis}
}

@Article{HanJau90,
  author =       Hansen_P #and# {B. Jaumard},
  title =        {Algorithms for the Maximum Satisfiability Problem},
  journal =      comp,
  year =         1990,
  volume =       44,
  pages =        {279--303}
}

@Article{HanMla01:ejor,
  title =        {Variable neighborhood search: Principles and applications},
  author =       Hansen_P #and# Mladenovic,
  journal =      ejor,
  volume =       130,
  number =       3,
  pages =        {449--467},
  year =         2001,
}

@Article{HanOst2001ec,
  author =       Hansen_N #and# {Ostermeier, A.},
  title =        {Completely derandomized self-adaptation in evolution
                  strategies},
  journal =      ec,
  year =         2001,
  volume =       9,
  pages =        {159--195},
  number =       2,
  doi =          {10.1162/106365601750190398},
  keywords =       {CMA-ES},
}

@Article{HanRosMauSchAug2011,
  author =       Hansen_N #and# {Raymond Ros and Nikolaus Mauny} #and# Schoenauer #and# Auger_A,
  title =        {Impacts of invariance in search: When {CMA-ES} and {PSO} face ill-conditioned and non-separable problems},
  journal =      asoco,
  year =         2011,
  volume =       11,
  number =       8,
  pages =        {5755--5769}
}

@Article{Hanne1999ejor,
  author =       {Thomas Hanne},
  journal =      ejor,
  title =        {On the convergence of multiobjective evolutionary algorithms},
  volume =       117,
  number =       3,
  pages =        {553--564},
  year =         1999,
  doi =          "10.1016/S0377-2217(98)00262-8",
  keywords =     "archiving, efficiency presserving"
}

@Article{Hanne2007ejor,
  title =        {A multiobjective evolutionary algorithm for approximating the
                  efficient set},
  author =       {Hanne, Thomas},
  journal =      ejor,
  year =         2007,
  number =       3,
  pages =        {1723--1734},
  volume =       176,
}

@Article{HarSaf2004,
  author =       {Hardin, Douglas P. and Saff, Edward B.},
  title =        {Discretizing Manifolds via Minimum Energy Points},
  journal =      {Notices of the American Mathematical Society},
  year =         2004,
  number =       10,
  pages =        {1186--1194},
  volume =       51,
  alias =        "Hardin2004",
}

@Article{HarSho87a,
  author =       {J. P. Hart and A. W. Shogan},
  title =        {Semi-greedy heuristics: An empirical study},
  journal =      orl,
  volume =       6,
  number =       3,
  pages =        {107--114},
  year =         1987
}

@Article{HarSim2016ec,
  author  = Hart_E #and# {Kevin Sim},
  title   = {A Hyper-Heuristic Ensemble Method for Static Job-Shop Scheduling},
  journal = ec,
  volume  = 24,
  number  = 4,
  pages   = {609--635},
  year    = 2016,
  doi     = {10.1162/EVCO_a_00183 }
}

@Article{Haraguchi2016:joh,
  author = 	 {Kazuya Haraguchi},
  title = 	 {Iterated Local Search with {Trellis}-Neighborhood for the Partial {Latin} Square Extension Problem},
  journal = 	 joh,
  year = 	 2016,
  volume = 	 22,
  number = 	 5,
  pages = 	 {727--757}
}

@Article{HasRaj2004,
  author   = { Sameer   Hasija  and  Chandrasekharan   Rajendran },
  title    = {Scheduling in flowshops to minimize total tardiness of jobs},
  journal  = ijpr,
  volume   = 42,
  number   = 11,
  pages    = {2289--2301},
  year     = 2004,
  doi      = {10.1080/00207540310001657595}
}

@Article{HasYagIba2008:do,
  author =       {Hideki Hashimoto} #and# Yagiura #and# Ibaraki,
  title =        {An Iterated Local Search Algorithm for the Time-dependent Vehicle
               Routing Problem with Time Windows},
  journal =      do,
  year =         2008,
  volume =       5,
  number =       2,
  pages =        {434--456}
}

@Article{Haykin2004nn,
  title =        {A comprehensive foundation},
  author =       {Haykin, Simon},
  journal =      nn,
  volume =       2,
  pages =        41,
  year =         2004
}

@Article{HazGunEre08customer,
  title =        {Customer order scheduling problem: a comparative
                  metaheuristics study},
  number =       5,
  journal =      ijamt,
  author =       "{\"O}nc{\"u} Hazir and Yavuz G{\"u}nalay and Erdal Erel",
  month =        may,
  year =         2008,
  keywords =     {{ACO,Customer} order {scheduling,Genetic}
                  {algorithms,Meta-heuristics,Simulated} {annealing,Tabu}
                  search},
  pages =        {589--598},
  volume =       37,
  doi =          {10.1007/s00170-007-0998-8},
  abstract =     {The customer order scheduling problem {(COSP)} is defined as
                  to determine the sequence of tasks to satisfy the demand of
                  customers who order several types of products produced on a
                  single machine. A setup is required whenever a product type
                  is launched.  The objective of the scheduling problem is to
                  minimize the average customer order flow time. Since the
                  customer order scheduling problem is known to be strongly
                  {NP-hard,} we solve it using four major metaheuristics and
                  compare the performance of these heuristics, namely,
                  simulated annealing, genetic algorithms, tabu search, and ant
                  colony optimization. These are selected to represent various
                  characteristics of metaheuristics: nature-inspired
                  vs. artificially created, population-based vs. local search,
                  etc. A set of problems is generated to compare the solution
                  quality and computational efforts of these heuristics.
                  Results of the experimentation show that tabu search and ant
                  colony perform better for large problems whereas simulated
                  annealing performs best in small-size problems. Some
                  conclusions are also drawn on the interactions between
                  various problem parameters and the performance of the
                  heuristics.},
}

@Article{HeYen2016many,
  title =        {Many-Objective Evolutionary Algorithm: Objective Space
                  Reduction and Diversity Improvement},
  author =       {He, Zhenan and Yen, Gary G.},
  journal =      tec,
  year =         2016,
  number =       1,
  pages =        {145--160},
  volume =       20,
}

@Article{HeZhaChu2021automl,
  author =       {Xin He and Kaiyong Zhao and Xiaowen Chu},
  title =        {{AutoML}: A survey of the state-of-the-art},
  journal =      kbs,
  volume =       212,
  pages =        106622,
  year =         2021,
  issn =         {0950-7051},
  doi =          {10.1016/j.knosys.2020.106622},
}

@Article{HelBraMos2013bound,
  author =       {Helwig, Sabine} #and# Branke #and# Mostaghim_S,
  title =        {Experimental Analysis of Bound Handling Techniques in
                  Particle Swarm Optimization},
  doi =          {10.1109/tevc.2012.2189404},
  year =         2013,
  month =        apr,
  volume =       17,
  number =       2,
  pages =        {259--271},
  journal =      tec,
  keywors =      "PSO; box-constraints; constraint handling; bounds"
}

@Article{HelKar1970,
  author =       {Held, Michael and Karp, Richard M.},
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  year =         1970
}

@Article{HelRen1998mp,
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  title =        "Solving quadratic (0,1)-problems by semidefinite programs and
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  number =       3,
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}

@Article{Helsgaun00,
  author =       Helsgaun,
  title =        {An Effective Implementation of the {Lin}-{Kernighan}
Traveling Salesman Heuristic},
  journal =      ejor,
  year =         2000,
  volume =       126,
  pages =        {106--130}
}

@Article{Helsgaun09,
  author =       Helsgaun,
  title =        {General {\it k}-opt Submoves for the {Lin}-{Kernighan} {TSP}
               Heuristic},
  journal =      mpc,
  year =         2009,
  volume =       1,
  number =       {2--3},
  pages =        {119--163}
}

@Article{Her2015toms,
  author =       {Michael A. Heroux},
  title =        {Editorial: {ACM} {TOMS} Replicated Computational Results
                  Initiative},
  doi =          {10.1145/2743015},
  year =         2015,
  month =        jun,
  publisher =    {Association for Computing Machinery ({ACM})},
  volume =       41,
  number =       3,
  pages =        {1--5},
  journal =      toms,
}

@Article{HerBlu2009:si,
  author =       {H. Hern{\'a}ndez} #and# Blum,
  title =        {Ant colony optimization for multicasting in static
                  wireless ad-hoc networks},
  journal =      swarm,
  volume =       3,
  number =       2,
  pages =        {125--148},
  year =         2009
}

@Article{HerColDua2021efficient,
  title =        {An efficient Variable Neighborhood Search for the Space-Free
                  Multi-Row Facility Layout problem},
  author =       {Herr{\'a}n, Alberto} #and# Colmenar_JM #and# Duarte_A,
  journal =      ejor,
  year =         2021,
  publisher =    {Elsevier},
  doi =          "10.1016/j.ejor.2021.03.027"
}

@Article{HerLozSan03:real-coded-crossover,
  author       = Herrera_F #and# Lozano_M #and# Sanchez_AM,
  title        = {A taxonomy for the crossover operator for real-coded genetic
                  algorithms: An experimental study},
  journal      = ijis,
  year         = 2003,
  volume       = 18,
  number       = 3,
  pages        = {309--338},
  doi          = {10.1002/int.10091}
}

@Article{HerLozVer1998air,
  author =       Herrera_F #and# Lozano_M #and# "Verdegay, J. L.",
  title =        "Tackling Real-Coded Genetic Algorithms: Operators
                  and Tools for Behavioural Analysis",
  journal =      airev,
  year =         1998,
  volume =       12,
  pages =        {265--319},
  keywords =     {genetic algorithms, real coding, continuous search
                  spaces, mutation, recombination}
}

@Article{HerSch2022hausdorff,
  title =        {A Bounded Archiver for {Hausdorff} Approximations of the
                  {Pareto} Front for Multi-Objective Evolutionary Algorithms},
  author =       Hernandez-Castellanos_CI #and# Schutze_O,
  journal =      mca,
  volume =       27,
  number =       3,
  pages =        48,
  year =         2022,
  doi =          "10.3390/mca27030048",
  publisher =    {Multidisciplinary Digital Publishing Institute}
}

@Article{HerSchSun2020noneps,
  author       = Hernandez-Castellanos_CI #and# Schutze_O #and# {Sun, J. Q.}
                  #and# { Ober-Bl\"obaum, S.},
  title        = {Non-epsilon dominated evolutionary algorithm for the set of
                  approximate solutions},
  journal      = mca,
  year         = 2020,
  volume       = 25,
  number       = 1,
  pages        = 3,
  keywords     = {archiving, multimodal}
}

@Article{HerWer1987tabucol,
  author       = Hertz_A #and# {de Werra, D.},
  title        = {Using Tabu Search Techniques for Graph Coloring},
  journal      = comp,
  year         = 1987,
  volume       = 39,
  number       = 4,
  pages        = {345--351}
}

@Article{Hermet2006ecj,
  author =       vanHemert_J,
  title =        {Evolving Combinatorial Problem Instances That Are Difficult
                  to Solve},
  journal =      ec,
  volume =       14,
  number =       4,
  pages =        {433--462},
  year =         2006,
  doi =          {10.1162/evco.2006.14.4.433},
  abstract =     {This paper demonstrates how evolutionary computation can be
                  used to acquire difficult to solve combinatorial problem
                  instances. As a result of this technique, the corresponding
                  algorithms used to solve these instances are
                  stress-tested. The technique is applied in three important
                  domains of combinatorial optimisation, binary constraint
                  satisfaction, Boolean satisfiability, and the travelling
                  salesman problem. The problem instances acquired through this
                  technique are more difficult than the ones found in popular
                  benchmarks. In this paper, these evolved instances are
                  analysed with the aim to explain their difficulty in terms of
                  structural properties, thereby exposing the weaknesses of
                  corresponding algorithms. }
}

@Article{HeuNieKru2020publish,
  author =       {Robert Heum{\"u}ller and Sebastian Nielebock and Jacob
                  Kr{\"u}ger and Frank Ortmeier},
  title =        {Publish or perish, but do not forget your software artifacts},
  doi =          {10.1007/s10664-020-09851-6},
  year =         2020,
  volume =       25,
  number =       6,
  pages =        {4585--4616},
  journal =      empsofteng,
}

@Article{Hic2006,
  author =       {Christian Hicks},
  title =        {A Genetic Algorithm tool for optimising cellular or
                  functional layouts in the capital goods industry},
  journal =      ijpe,
  volume =       104,
  number =       2,
  pages =        {598--614},
  year =         2006,
  doi =          {10.1016/j.ijpe.2005.03.010}
}

@Article{HieLiLiuPar2020,
  title =        {Many-objective test suite generation for software product
                  lines},
  author =       {Hierons, Robert M.} #and# Li_Miqing #and# {Liu, Xiaohui and
                  Parejo, Jose Antonio and Segura, Sergio} #and# Yao_Xin,
  journal =      acm-tosem,
  year =         2020,
  number =       1,
  pages =        {1--46},
  volume =       29,
}

@Article{HinSal2006autoencoder,
  title =        {Reducing the dimensionality of data with neural networks},
  author =       {Hinton, Geoffrey E. and Salakhutdinov, Ruslan R.},
  journal =      science,
  volume =       313,
  number =       5786,
  pages =        {504--507},
  year =         2006,
}

@Article{Hoeffding1963,
  title =        {Probability inequalities for sums of bounded random
                  variables},
  author =       {Hoeffding, Wassily},
  journal =      jasa,
  volume =       58,
  number =       301,
  pages =        {13--30},
  year =         1963,
  alias =        {hoeffding1963probability}
}

@Article{HonKahMoo1997,
  author =       {I. Hong and A. B. Kahng and B. R. Moon},
  title =        {Improved large-step {Markov} chain variants for the symmetric {TSP}},
  journal =      joh,
  year =         1997,
  volume =       3,
  number =       1,
  pages =        {63--81}
}

@Article{Hoo1994or,
  author       = Hooker_JN,
  title        = {Needed: An Empirical Science of Algorithms},
  journal      = or,
  year         = 1994,
  volume       = 42,
  number       = 2,
  pages        = {201--212}
}

@Article{Hoo1996joh,
  author       = Hooker_JN,
  title        = {Testing Heuristics: We Have It All Wrong},
  journal      = joh,
  year         = 1996,
  volume       = 1,
  number       = 1,
  pages        = {33--42},
  doi          = {10.1007/BF02430364}
}

@Article{Hoo2012,
  title =        {Generalized functional {ANOVA} diagnostics for
                  high-dimensional functions of dependent variables},
  author =       {Hooker, Giles},
  journal =      jcgs,
  year =         2012,
  volume =       16,
  number =       3,
  pages =        {709--732},
  doi =          {10.1198/106186007X237892},
}

@Article{HooLinSch2014claspfolio2,
  title =        {Claspfolio 2: Advances in Algorithm Selection for Answer Set
                  Programming},
  author =       Hoos #and# Lindauer_M #and# {Schaub, Torsten},
  journal =      tplp,
  volume =       14,
  number =       {4-5},
  pages =        {560--585},
  year =         2014,
  publisher =    cup-pub,
}

@Article{HooStu2014,
  author =       Hoos #and# Stuetzle,
  title =        {On the Empirical Scaling of Run-time for Finding Optimal
                  Solutions to the Traveling Salesman Problem},
  journal =      ejor,
  year =         2014,
  volume =       238,
  number =       1,
  pages =        {87--94}
}

@Article{HooStu2015:ol,
  author =       Hoos #and# Stuetzle,
  title =        {On the Empirical Time Complexity of Finding Optimal Solutions
                  vs.\ Proving Optimality for {Euclidean} {TSP} Instances},
  journal =      ol,
  year =         2015,
  volume =       9,
  number =       6,
  pages =        {1247--1254}
}

@Article{Hoos:PbO,
  author =       Hoos,
  title =        {Programming by optimization},
  journal =      cacm,
  volume =       55,
  number =       2,
  month =        feb,
  year =         2012,
  pages =        {70--80},
  numpages =     11,
  doi =          {10.1145/2076450.2076469},
}

@Article{HotTanTie2020deep,
  author       = {Hottung, Andr{\'e} and Tanaka, Shunji} #and# Tierney,
  title        = {Deep learning assisted heuristic tree search for the
                  container pre-marshalling problem},
  journal      = cor,
  year         = 2020,
  volume       = 113,
  pages        = 104781,
  doi          = {10.1016/j.cor.2019.104781}
}

@Article{HotTie2022nlns,
  author       = {Hottung, Andr{\'e}} #and# Tierney,
  title        = {Neural large neighborhood search for routing problems},
  journal      = aij,
  year         = 2022,
  volume       = 313,
  pages        = 103786,
  month        = dec,
  doi          = {10.1016/j.artint.2022.103786}
}

@Article{HozDjuHoz2005,
  title =        {Estimating the mean and variance from the median, range, and
                  the size of a sample},
  author =       {Hozo, Stela Pudar and Djulbegovic, Benjamin and Hozo, Iztok},
  journal =      bmc-mrm,
  volume =       5,
  number =       1,
  pages =        13,
  year =         2005,
}

@Article{HuKahTsa95,
  author =       {T. C. Hu and A. B. Kahng and C.-W. A. Tsao},
  title =        {Old Bachelor Acceptance: A New Class of Non-Monotone
                  Threshold Accepting Methods},
  journal =      orsa,
  year =         1995,
  volume =       7,
  number =       4,
  pages =        {417--425}
}

@Article{HuWanQiu2018nca,
  author =       {Hu, Wenbin and Wang, Huan and Qiu, Zhenyu and Nie, Cong and
                  Yan, Liping},
  doi =          {10.1007/s00521-016-2508-0},
  journal =      nca,
  keywords =     {BML,Optimization,Simulation,Traffic congestion,Updating
                  rules},
  title =        {A quantum particle swarm optimization driven urban traffic
                  light scheduling model},
  year =         2018,
  alias = "Hu2018"
}

@Article{HuYanWang2017,
  author =       {Hu, Wenbin and Yan, Liping and Wang, Huan and Du, Bo and Tao,
                  Dacheng},
  journal =      is,
  keywords =     {BML model,Prediction,Real-time,Traffic jam,Urban traffic
                  network},
  title =        {Real-time traffic jams prediction inspired by {Biham},
                  {Middleton} and {Levine} (BML) model},
  year =         2017,
  alias =        "Hu2017"
}

@Article{HuaAllNot2006global,
  title =        {Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models},
  author =       {Huang, Deng and Allen, Theodore T. and Notz, William I. and
                  Zeng, Ning},
  journal =      jgo,
  volume =       34,
  number =       3,
  pages =        {441--466},
  year =         2006,
  publisher =    {Springer},
  doi =          {10.1007/s10898-005-2454-3}
}

@Article{HuaLiYao2020algconf,
  author =       {Changwu Huang and Yuanxiang Li} #and# Yao_Xin,
  title =        {A Survey of Automatic Parameter Tuning Methods for
                  Metaheuristics},
  journal =      tec,
  volume =       24,
  number =       2,
  pages =        {201--216},
  year =         2020,
  doi =          {10.1109/TEVC.2019.2921598},
}

@Article{HubHinBar2006tec,
  title =        {A Review of Multiobjective Test Problems and a Scalable Test
                  Problem Toolkit},
  volume =       10,
  doi =          {10.1109/TEVC.2005.861417},
  number =       5,
  journal =      tec,
  author =       {Huband, S. and Hingston, P. and Barone, L. and While, L.},
  year =         2006,
  pages =        {477--506},
  annote =       "Proposed the WFG benchmark suite"
}

@Article{HubLukHog1997,
  author =	 {B. Huberman and R. Lukose and T. Hogg},
  title =	 {An Economic Approach to Hard Computational Problems},
  journal =	 science,
  year =	 1997,
  volume =	 275,
  pages =	 {51--54}
}

@Article{HueRioRui2017,
  author = 	 {D. L. Huerta-Mu{\~n}oz and R. Z. R{\'i}os-Mercado} #and# Ruiz_Ruben,
  title = 	 {An Iterated Greedy Heuristic for a Market Segmentation Problem with Multiple Attributes},
  journal = 	 ejor,
  year = 	 2017,
  volume = 	 261,
  number = 	 1,
  pages = 	 {75--87}
}

@Article{HumLieTal2013joh,
  author =       Humeau #and# Liefooghe #and# Talbi #and# Verel,
  title =        "{ParadisEO-MO}: From Fitness Landscape Analysis to
                  Efficient Local Search Algorithms",
  volume =       19,
  doi =          {10.1007/s10732-013-9228-8},
  number =       6,
  journal =      joh,
  month =        jun,
  year =         2013,
  pages =        {881--915},
}

@Article{HunJosMel2009dace,
  title =        {Design and Analysis of Computer Experiments With Branching
                  and Nested Factors},
  author =       {Hung, Ying and Joseph, V. Roshan and Melkote, Shreyes N.},
  journal =      techms,
  year =         2009,
  number =       4,
  pages =        {354--365},
  volume =       51,
  alias =        "Hung2009",
  doi =          {10.1198/TECH.2009.07097}
}

@Article{HurMau2010,
  author =       {M. Hurtgen and J.-C. Maun},
  title =        {Optimal {PMU} placement using Iterated Local Search},
  journal =      {International Journal of Electrical Power \& Energy Systems},
  year =         2010,
  volume =       32,
  number =       8,
  pages =        {857--860}
}

@Article{Hurlbert1984,
  author =       {S. H. Hurlbert},
  title =        {Pseudoreplication and the Design of Ecological Field
                  Experiments},
  journal =      {Ecological Monographs},
  volume =       54,
  number =       2,
  pages =        {187--211},
  year =         1984,
}

@Article{HusStu2014cor,
  author =       "Mohamed Saifullah Hussin" #and# Stuetzle,
  title =        "Tabu Search vs. Simulated Annealing for Solving
                  Large Quadratic Assignment Instances",
  journal =      cor,
  volume =       43,
  pages =        "286--291",
  year =         2014
}

@Article{HutHooLey2010amai,
  author =       Hutter #and# Hoos #and# Leyton-Brown,
  title =        {Tradeoffs in the Empirical Evaluation of Competing Algorithm Designs},
  journal =      amai,
  year =         2010,
  volume =       60,
  number =       {1--2},
  pages =        {65--89}
}

@Article{HutHooLey2013arxiv,
  author    = Hutter #and# Hoos #and# Leyton-Brown,
  title     = {Bayesian Optimization With Censored Response Data},
  journal   = arxiv # {1310.1947},
  year      = 2013,
  url       = {http://arxiv.org/abs/1310.1947}
}

@Article{HutHooLeyStu2009jair,
  author =       Hutter #and# Hoos #and# Leyton-Brown #and# Stuetzle,
  title =        "{\softwarepackage{ParamILS}:} An Automatic Algorithm Configuration Framework",
  journal =      jair,
  year =         2009,
  volume =       36,
  pages =        "267--306",
  month =        oct,
  doi = 	 {10.1613/jair.2861}
}

@Article{HutLinBal2017aij,
  author =       Hutter #and# Lindauer_M #and# {Adrian Balint and Sam Bayless}
                  #and# Hoos #and# Leyton-Brown,
  title =        {The Configurable {SAT} Solver Challenge {(CSSC)}},
  journal =      aij,
  year =         2017,
  volume =       243,
  pages =        {1--25},
  alias =        "HutLinBal++2017:aij",
  doi =          {10.1016/j.artint.2016.09.006}
}

@Article{HutXuHooLey2014,
  author       = Hutter #and# Xu_Lin #and# Hoos #and# Leyton-Brown,
  title        = {Algorithm Runtime Prediction: Methods \& evaluation},
  journal      = aij,
  year         = 2014,
  volume       = 206,
  pages        = {79--111},
  doi          = {10.1016/j.artint.2013.10.003},
  abstract     = {Perhaps surprisingly, it is possible to predict how long an
                  algorithm will take to run on a previously unseen input,
                  using machine learning techniques to build a model of the
                  algorithm's runtime as a function of problem-specific
                  instance features. Such models have important applications to
                  algorithm analysis, portfolio-based algorithm selection, and
                  the automatic configuration of parameterized algorithms. Over
                  the past decade, a wide variety of techniques have been
                  studied for building such models. Here, we describe
                  extensions and improvements of existing models, new families
                  of models, and---perhaps most importantly---a much more thorough
                  treatment of algorithm parameters as model inputs. We also
                  comprehensively describe new and existing features for
                  predicting algorithm runtime for propositional satisfiability
                  (SAT), travelling salesperson (TSP) and mixed integer
                  programming (MIP) problems. We evaluate these innovations
                  through the largest empirical analysis of its kind, comparing
                  to a wide range of runtime modelling techniques from the
                  literature. Our experiments consider 11 algorithms and 35
                  instance distributions; they also span a very wide range of
                  SAT, MIP, and TSP instances, with the least structured having
                  been generated uniformly at random and the most structured
                  having emerged from real industrial applications. Overall, we
                  demonstrate that our new models yield substantially better
                  runtime predictions than previous approaches in terms of
                  their generalization to new problem instances, to new
                  algorithms from a parameterized space, and to both
                  simultaneously.},
  keywords     = {Empirical performance models; Mixed integer programming; SAT}
}

@Article{IOHanalyzer,
  author       = Wang_H #and# Vermetten_D #and# {Furong Ye} #and# Doerr_C
                  #and# Baeck_Thomas,
  title        = {{IOHanalyzer}: Detailed Performance Analyses for Iterative
                  Optimization Heuristics},
  journal      = telo,
  year         = 2022,
  volume       = 2,
  number       = 1,
  pages        = {3:1--3:29},
  doi          = {10.1145/3510426}
}

@Article{IOHexperimenter2021,
  author       = {Jacob de Nobel and Furong Ye} #and# Vermetten_D #and# Wang_H #and# Doerr_C #and# Baeck_Thomas,
  title        = {{IOHexperimenter}: Benchmarking Platform for Iterative
                  Optimization Heuristics},
  journal      = arxiv # {2111.04077},
  year         = 2021,
  annote       = {Published in ECJ~\cite{IOHexperimenter2024}},
  doi          = {10.48550/arXiv.2111.04077}
}

@Article{IOHexperimenter2024,
  author       = {Jacob de Nobel and Furong Ye} #and# Vermetten_D #and# Wang_H #and# Doerr_C #and# Baeck_Thomas,
  title        = {{IOHexperimenter}: Benchmarking Platform for Iterative
                  Optimization Heuristics},
  journal      = ec,
  year         = 2024,
  pages        = {1--6},
  doi          = {10.1162/evco_a_00342},
}

@Article{IOHprofiler,
  author       = Doerr_C #and# Wang_H #and# {Furong Ye} #and# {van Rijn,
                  Sander} #and# Baeck_Thomas,
  title        = {{IOHprofiler}: A Benchmarking and Profiling Tool for
                  Iterative Optimization Heuristics},
  journal      = arxiv # {1806.07555},
  year         = 2018,
  month        = oct,
  keywords     = {Benchmarking; Heuristics},
  doi          = {10.48550/arXiv.1810.05281}
}

@Article{IacPal2012:si,
  author =       {Claudio Iacopino and Phil Palmer},
  title =        {The Dynamics of Ant Colony Optimization Algorithms Applied to Binary Chains},
  journal =      swarm,
  year =         2012,
  volume =       6,
  number =       4,
  pages =        {343--377}
}

@Article{IacPalPol2014ants,
  journal =      actafutura,
  title =        {How Ants Can Manage Your Satellites},
  doi =          {10.2420/AF09.2014.59},
  volume =       9,
  author =       {Iacopino, Claudio and Palmer, Phil and Policella, N. and
                  Donati, A. and Brewer, A.},
  pages =        {59--72},
  year =         2014,
  keywords = "ACO, Space"
}

@Article{IbaImaNon++2008:dam,
  author =       {Toshihide Ibaraki and Shinji Imahori and Koji Nonobe and Kensuke Sobue and Takeaki Uno} #and# Yagiura,
  title =        {An Iterated Local Search Algorithm for the Vehicle Routing Problem
               with Convex Time Penalty Functions},
  journal =      dam,
  year =         2008,
  volume =       156,
  number =       11,
  pages =        {2050--2069}
}

@Article{Ibaraki2010:itor,
  author = 	 {Toshihide Ibaraki},
  title = 	 {A Personal Perspective on Problem Solving by General Purpose Solvers},
  journal = 	 itor,
  year = 	 2010,
  volume = 	 17,
  number = 	 3,
  pages = 	 {303--315}
}

@Article{IdeSch2016,
  author =       "Ide, Jonas" #and# Schobel_A,
  title =        "Robustness for uncertain multi-objective optimization: a
                  survey and analysis of different concepts",
  journal =      orspec,
  year =         2016,
  volume =       38,
  number =       1,
  pages =        "235--271",
  abstract =     "In this paper, we discuss various concepts of robustness for
                  uncertain multi-objective optimization problems. We extend
                  the concepts of flimsily, highly, and lightly robust
                  efficiency and we collect different versions of minmax robust
                  efficiency and concepts based on set order relations from the
                  literature. Altogether, we compare and analyze ten different
                  concepts and point out their relations to each
                  other. Furthermore, we present reduction results for the
                  class of objective-wise uncertain multi-objective
                  optimization problems.",
  doi =          "10.1007/s00291-015-0418-7",
}

@Article{IgeHanRot2007ec,
  author =       Igel_C #and# Hansen_N #and# {S. Roth},
  title =        {Covariance Matrix Adaptation for Multi-objective
                  Optimization},
  journal =      ec,
  year =         2007,
  volume =       15,
  pages =        {1--28},
  number =       1
}

@Article{IgeHeiGla2008jmlr,
  author =       Igel_C #and# {V. Heidrich-Meisner} #and# Glasmachers,
  title =        {Shark},
  journal =      jmlr,
  year =         2008,
  volume =       9,
  month =        jun,
  pages =        {993--996},
  url = {http://www.jmlr.org/papers/volume9/igel08a/igel08a.pdf},
  alias =        {Igel:2008ut}
}

@Article{Ilich98,
  author =       Ilich #and# Simonovic ,
  title =        "Evolutionary Algorithm for minimization of pumping
                  cost",
  journal =      jcce,
  year =         1998,
  volume =       12,
  number =       4,
  pages =        {232--240},
  month =        oct,
  abstract =     "This paper deals with minimizing the total cost of
                  pumping in a liquid pipeline. Previous experience
                  with the most common solution procedures in pipeline
                  optimization is discussed along with their strengths
                  and weaknesses. The proposed method is an
                  evolutionary algorithm with two distinct features:
                  (1) The search is restricted to feasible region
                  only; and (2) it utilizes a floating point decision
                  variable rather than integer or binary as is the
                  case with most other similar approaches. A numerical
                  example is presented as a basis for verification of
                  the proposed method and its comparison with the
                  existing solver that utilizes the nonlinear
                  Newtonian search. The proposed method provides
                  promising improvements in terms of optimality when
                  compared to the widespread gradient search methods
                  because it does not involve evaluation of the
                  gradient of the objective function. It also provides
                  potential to improve the performance of previous
                  evolutionary programs because it restricts the
                  search to the feasible region, thus eliminating
                  large overhead associated with generation and
                  inspection of solutions that are
                  infeasible. Comparison of the two solutions revealed
                  improvement of the solution in favor of the proposed
                  algorithm, which ranged up to 6\% depending on the
                  initial values of the decision variables in the
                  Newtonian search. The proposed method was not
                  sensitive to the starting value of the decision
                  variables.",
}

@Article{ImaYagNag2009:ejor,
  author       = {Takashi Imamichi} #and# Yagiura #and# {Hiroshi Nagamochi},
  title        = {An Iterated Local Search Algorithm Based on Nonlinear
                  Programming for the Irregular Strip Packing Problem},
  journal      = do,
  year         = 2009,
  volume       = 6,
  number       = 4,
  pages        = {345--361}
}

@Article{Inselberg1985pc,
  title =        {The Plane with Parallel Coordinates},
  author =       {Inselberg, Alfred},
  journal =      {The {Visual} {Computer}},
  volume =       1,
  number =       2,
  pages =        {69--91},
  year =         1985,
}

@Article{Ioa2005why,
  title =        {Why Most Published Research Findings Are False},
  author =       Ioannidis,
  journal =      plosmed,
  volume =       2,
  number =       8,
  doi =          "10.1371/journal.pmed.0020124",
  pages =        {e124},
  year =         2005,
}

@Article{Irnich2008,
  author = 	 {Stefan Irnich},
  title = 	 {A Unified Modeling and Solution Framework for Vehicle Routing and
               Local Search-Based Metaheuristics},
  journal = 	 informsjoc,
  year = 	 2008,
  volume = 	 20,
  number = 	 2,
  pages = 	 {270--287}
}

@Article{IruCalLoz2016mallows,
  author =       Irurozki_E #and# {Borja Calvo} #and# Lozano_JA,
  title =        {Sampling and Learning {Mallows} and Generalized {Mallows}
                  Models Under the {Cayley} Distance},
  doi =          {10.1007/s11009-016-9506-7},
  year =         2016,
  month =        jun,
  volume =       20,
  number =       1,
  pages =        {1--35},
  journal =      mcap,
}

@Article{IruCalLoz2016permallows,
  title =        {{\rpackage{PerMallows}}: An {\proglang{R}} Package for Mallows
                  and Generalized Mallows Models},
  author =       Irurozki_E #and# {Calvo, Borja} #and# Lozano_JA,
  abstract =     {In this paper we present the R package PerMallows, which is a
                  complete toolbox to work with permutations, distances and
                  some of the most popular probability models for permutations:
                  Mallows and the Generalized Mallows models. The Mallows model
                  is an exponential location model, considered as analogous to
                  the Gaussian distribution. It is based on the definition of a
                  distance between permutations. The Generalized Mallows model
                  is its best-known extension. The package includes functions
                  for making inference, sampling and learning such
                  distributions. The distances considered in PerMallows are
                  Kendall's $\tau$, Cayley, Hamming and Ulam.},
  doi =          {10.18637/jss.v071.i12},
  issn =         15487660,
  journal =      jss,
  keywords =     {Cayley,Generalized Mallows,Hamming,Kendall's
                  $\tau$,Learning,Mallows,Permutation,R,Ranking,Sampling,Ulam},
  volume =       71,
  year =         2019
}

@Article{IruLobPer2020arxiv,
  title =        {Rank aggregation for non-stationary data streams},
  author =       Irurozki_E #and# {Lobo, Jesus and Perez, Aritz and Del Ser,
                  Javier},
  keywords =     {uborda},
  journal =      arxiv # {1910.08795 [stat.ML]},
  year =         2020,
  url =          {https://arxiv.org/abs/1910.08795}
}

@Article{Ish1998,
  author =       Ishibuchi #and# Murata,
  title =        "A multi-objective genetic local search algorithm and its
                  application to flowshop scheduling",
  journal =      tsmcc,
  number =       3,
  pages =        {392--403},
  volume =       28,
  year =         1998
}

@Article{IshAkeNoj2015tec,
  author =       Ishibuchi #and# {N. Akedo and Y. Nojima},
  journal =      tec,
  title =        {Behavior of Multiobjective Evolutionary Algorithms on
                  Many-Objective Knapsack Problems},
  year =         2015,
  volume =       19,
  number =       2,
  pages =        {264--283},
  doi =          {10.1109/TEVC.2014.2315442},
}

@Article{IshImaSet2018refpoint,
  title =        {How to specify a reference point in hypervolume calculation
                  for fair performance comparison},
  author =       Ishibuchi #and# {Imada, Ryo and Setoguchi, Yu and
                  Nojima, Yusuke},
  journal =      ec,
  year =         2018,
  number =       3,
  pages =        {411--440},
  volume =       26
}

@Article{IshMisTan1995,
  title =        {Modified simulated annealing algorithms for the flow shop
                  sequencing problem},
  author =       Ishibuchi #and# {Misaki, Shinta and Tanaka, Hideo},
  journal =      ejor,
  volume =       81,
  number =       2,
  pages =        {388--398},
  year =         1995,
}

@Article{IshSetMas2017shape,
  title =        {Performance of decomposition-based many-objective algorithms
                  strongly depends on {Pareto} front shapes},
  author =       Ishibuchi #and# {Setoguchi, Yu and Masuda, Hiroyuki and
                  Nojima, Yusuke},
  journal =      tec,
  year =         2017,
  number =       2,
  pages =        {169--190},
  volume =       21,
  publisher =    {IEEE}
}

@Article{IviTha2019repro,
  author =       {Peter Ivie and Douglas Thain},
  title =        {Reproducibility in Scientific Computing},
  doi =          {10.1145/3186266},
  year =         2019,
  publisher =    {Association for Computing Machinery ({ACM})},
  volume =       51,
  number =       3,
  pages =        {1--36},
  journal =      acm-cs,
}

@Article{IyeSax2004,
  author =       {Srikanth K. Iyer and Barkha Saxena},
  title =        {Improved genetic algorithm for the permutation flowshop
                  scheduling problem},
  journal =      cor,
  volume =       31,
  number =       4,
  pages =        {593--606},
  year =         2004,
  doi =          {10.1016/S0305-0548(03)00016-9}
}

@Article{IzzBecMyaNas2007search,
  author       = Izzo_D #and# {Becerra, V. M. and Myatt, D. R. and Nasuto,
                  S. J. and Bishop, J. M.},
  title        = {Search space pruning and global optimisation of multiple
                  gravity assist spacecraft trajectories},
  journal      = jgo,
  year         = 2007,
  volume       = 38,
  pages        = {283--296},
  publisher    = {Springer},
  doi          = {10.1007/s10898-006-9106-0}
}

@Article{Izzo2015lambert,
  author       = Izzo_D,
  title        = {Revisiting {Lambert}'s Problem},
  journal      = celmech,
  year         = 2015,
  volume       = 121,
  pages        = {1--15},
  publisher    = {Springer}
}

@Article{Jac2011jss,
  title =        "Multi-State Models for Panel Data: The {\rpackage{msm}} Package for \proglang{R}",
  author =       {Christopher H. Jackson},
  journal =      jss,
  year =         2011,
  volume =       38,
  number =       8,
  pages =        {1--29},
  url =          {http://www.jstatsoft.org/v38/i08/},
}

@Article{JacBogNas1991,
  author = 	 {Richard H. F. Jackson and Paul T. Boggs and Stephen G. Nash and Susan Powell},
  title = 	 {Guidelines for Reporting Results of Computational Experiments. Report of the Ad
		  Hoc Committee},
  journal = 	 mp,
  year = 	 1991,
  volume = 	 49,
  number = 	 3,
  pages = 	 {413--425}
}

@Article{JacBru1995:ig,
  author =       {Jacobs, Larry W. and Brusco, Michael J.},
  title =        {A Local Search Heuristic for Large Set-Covering
                  Problems},
  journal =      nrl,
  year =         1995,
  volume =       42,
  number =       7,
  pages =        {1129--1140}
}

@Article{JacKah1995anchoring,
  author       = {Jacowitz, Karen E.} #and# Kahneman,
  title        = {Measures of {Anchoring} in {Estimation} {Tasks}},
  journal      = {Personality and Social Psychology Bulletin},
  year         = 1995,
  volume       = 21,
  number       = 11,
  pages        = {1161--1166},
  month        = nov,
  issn         = {0146-1672},
  doi          = {10.1177/01461672952111004},
  abstract     = {The authors describe a method for the quantitative study of
                  anchoring effects in estimation tasks. A calibration group
                  provides estimates of a set of uncertain quantities. Subjects
                  in the anchored condition first judge whether a specified
                  number (the anchor) is higher or lower than the true value
                  before estimating each quantity. The anchors are set at
                  predetermined percentiles of the distribution of estimates in
                  the calibration group (15th and 85th percentiles in this
                  study). This procedure permits the transformation of anchored
                  estimates into percentiles in the calibration group, allows
                  pooling of results across problems, and provides a natural
                  measure of the size of the effect. The authors illustrate the
                  method by a demonstration that the initial judgment of the
                  anchor is susceptible to an anchoring-like bias and by an
                  analysis of the relation between anchoring and subjective
                  confidence.},
  language     = {en}
}

@Article{JacOzcJoh2018,
  author =       {Warren G. Jackson} #and# Ozcan_E #and# {Robert I. John},
  title =        {Move acceptance in local search metaheuristics for cross-domain search},
  journal =      eswa,
  volume =       109,
  pages =        {131--151},
  year =         2018
}

@Article{JaeParKip2008ejor,
  title   ={The development of a multi-objective Tabu Search algorithm for continuous optimisation problems},
  author  ={Daniel M Jaeggi and Geoffrey T Parks and  Timoleon Kipouros and P John Clarkson},
  journal =ejor,
  volume  =185,
  number  =3,
  pages   ={1192--1212},
  year    =2008
}

@Article{JajMinHarPro1992,
  title =        {{CLASS}: computerized layout solutions using simulated annealing},
  author =       {Jajodia, Satish and Minis, Ioannis and Harhalakis, George and Proth, Jean-Marie},
  journal =      ijpr,
  volume =       30,
  number =       1,
  pages =        {95--108},
  year =         1992,
  publisher =    taylorfrancis
}

@Article{Jas2002,
  title=         {Genetic local search for multi-objective combinatorial optimization},
  author =       Jaszkiewicz,
  journal =      ejor,
  volume =       137,
  number =       1,
  pages =        {50--71},
  year =         2002,
}

@Article{Jas2018ejor,
  title =        {Many-Objective {Pareto} Local Search},
  author =       Jaszkiewicz,
  journal =      ejor,
  year =         2018,
  volume =       271,
  number =       3,
  pages =        {1001--1013},
  doi =          {10.1016/j.ejor.2018.06.009}
}

@Article{JasLus2018ndtree,
  title =        {{ND}-tree-based update: a fast algorithm for the dynamic
                  nondominance problem},
  author =       Jaszkiewicz #and# Lust,
  journal =      tec,
  year =         2018,
  number =       5,
  pages =        {778--791},
  volume =       22,
  publisher =    {IEEE}
}

@Article{Jasz02mogls,
  author =       Jaszkiewicz,
  title =        {On the performance of multiple-objective genetic
                  local search on the 0/1 knapsack problem -- A
                  comparative experiment},
  journal =      tec,
  year =         2002,
  volume =       6,
  number =       4,
  pages =        {402--412},
}

@Article{Jen03,
  title =        "Reducing the run-time complexity of multiobjective
                  {EA}s: The {NSGA-II} and other algorithms",
  author =       Jensen,
  journal =      tec,
  volume =       7,
  number =       5,
  pages =        {503--515},
  year =         2003
}

@Article{Jensen2004,
  author =       Jensen,
  title =        {Helper-Objectives: {Using} Multi-Objective Evolutionary
                  Algorithms for Single-Objective Optimisation},
  journal =      jmma,
  year =         2004,
  volume =       3,
  number =       4,
  pages =        {323--347},
  keywords =     "multi-objectivization"
}

@Article{JerSor1998dam,
  title =        {The {Metropolis} algorithm for graph bisection},
  author =       Jerrum_M #and# {Sorkin, Gregory},
  journal =      dam,
  volume =       82,
  number =       1,
  pages =        {155--175},
  year =         1998,
  publisher =    elsevier
}

@Article{Jerrum1992,
  title =        {Large cliques elude the {Metropolis} process},
  author =       Jerrum_M,
  journal =      rsa,
  volume =       3,
  number =       4,
  pages =        {347--359},
  year =         1992,
  publisher =    jws
}

@Article{JiaOngZhaFen2014metrics,
  author =       {S. Jiang and Y. S. Ong and J. Zhang and L. Feng},
  journal =      tcyb,
  title =        {Consistencies and Contradictions of Performance Metrics in
                  Multiobjective Optimization},
  year =         2014,
  volume =       44,
  number =       12,
  pages =        {2391--2404},
}

@Article{JiaZouYanYao2022dynamic,
  author =       {Jiang, Shouyong and Zou, Juan and Yang, Shengxiang} #and# Yao_Xin,
  title =        {Evolutionary Dynamic Multi-Objective Optimisation: A Survey},
  year =         2022,
  volume =       55,
  number =       4,
  doi =          {10.1145/3524495},
  journal =      acm-cs,
  month =        nov,
  articleno =    76,
  numpages =     47,
  keywords =     {evolutionary algorithm, evolutionary dynamic multi-objective
                  optimisation, dynamic environment, Multi-objective
                  optimisation}
}

@Article{Jin2005fitness,
  author =       Jin_Yaochu,
  title =        {A Comprehensive Survey of Fitness Approximation in
                  Evolutionary Computation},
  journal =      soco,
  year =         2005,
  volume =       9,
  number =       1,
  pages =        {3--12},
  pdf =          {Jin05 - Fitness approximation in EA.pdf}
}

@Article{Jin2011surrogate,
  author =       Jin_Yaochu,
  title =        {Surrogate-Assisted Evolutionary Computation: Recent
                  Advances and Future Challenges},
  shorttitle =   {Surrogate-Assisted Evolutionary Computation},
  year =         2011,
  month =        jun,
  journal =      swevo,
  volume =       1,
  number =       2,
  pages =        {61--70},
  issn =         {2210-6502},
  doi =          {10.1016/j.swevo.2011.05.001},
  abstract =     {Surrogate-assisted, or meta-model based evolutionary
                  computation uses efficient computational models, often known
                  as surrogates or meta-models, for approximating the fitness
                  function in evolutionary algorithms. Research on
                  surrogate-assisted evolutionary computation began over a
                  decade ago and has received considerably increasing interest
                  in recent years. Very interestingly, surrogate-assisted
                  evolutionary computation has found successful applications
                  not only in solving computationally expensive single- or
                  multi-objective optimization problems, but also in addressing
                  dynamic optimization problems, constrained optimization
                  problems and multi-modal optimization problems. This paper
                  provides a concise overview of the history and recent
                  developments in surrogate-assisted evolutionary computation
                  and suggests a few future trends in this research area.},
  langid =       {english},
  keywords =     {Evolutionary computation,Expensive optimization
                  problems,Machine learning,Meta-models,Model
                  management,Surrogates}
}

@Article{JinWanChu2019data,
  author =       Jin_Yaochu #and# {Handing Wang} #and# Chugh_Tinkle #and# {Dan
                  Guo} #and# Miettinen,
  journal =      tec,
  title =        {Data-Driven Evolutionary Optimization: An Overview and Case
                  Studies},
  year =         2019,
  month =        jun,
  number =       3,
  pages =        {442--458},
  volume =       23,
  doi =          {10.1109/tevc.2018.2869001},
}

@Article{JinWon2010adapt,
  title =        {Adaptive, convergent, and diversified archiving strategy for
                  multiobjective evolutionary algorithms},
  author =       {Jin, Huidong and Wong, Man-Leung},
  journal =      eswa,
  year =         2010,
  number =       12,
  pages =        {8462--8470},
  volume =       37,
  publisher =    {Elsevier}
}

@Article{Joh54,
  author =       Johnson_DS,
  title =        "Optimal Two- and Three-stage Production Scheduling
                  with Setup Times Included",
  journal =      nrlq,
  year =         1954,
  volume =       1,
  pages =        {61--68}
}

@Article{JohAraMcGSch1989,
  author       = Johnson_DS #and# {Cecilia R. Aragon} #and# McGeoch_LA #and#
                  {Catherine Schevon},
  title        = {Optimization by Simulated Annealing: An Experimental
                  Evaluation: Part {I}, Graph Partitioning},
  journal      = or,
  year         = 1989,
  volume       = 37,
  number       = 6,
  pages        = {865--892},
  doi          = {10.1287/opre.39.3.378}
}

@Article{JohAraMcGSch1991,
  author =       Johnson_DS #and# {Cecilia R. Aragon} #and# McGeoch_LA #and# {Catherine Schevon},
  title =        {Optimization by Simulated Annealing: An Experimental
                  Evaluation: Part {II}, Graph Coloring and Number Partitioning},
  journal =      or,
  year =         1991,
  volume =       39,
  number =       3,
  pages =        {378--406}
}

@Article{JohJac2002:dam,
  title =        {On the Convergence of Generalized Hill Climbing Algorithms},
  author =       Johnson_AW #and# Jacobson_SH,
  journal =      dam,
  volume =       119,
  number =       1,
  pages =        {37--57},
  year =         2002,
  publisher =    elsevier
}

@Article{JohMooYlv1990,
  title =        {Minimax and maximin distance designs},
  author =       {Johnson, Mark E. and Moore, Leslie M. and Ylvisaker, Donald},
  journal =      jspi,
  year =         1990,
  number =       2,
  pages =        {131--148},
  volume =       26,
  keywords =     {Bayesian design},
  alias =        "Johnson1990b"
}

@Article{JohPapYan1988,
  author =       Johnson_DS #and# Papadimitriou #and# Yannakakis,
  title =        {How Easy is Local Search?},
  journal =      jcss,
  year =         1988,
  volume =       37,
  number =       1,
  pages =        {79--100}
}

@Article{JonKriMicSch2023generalized,
  title =        {Generalized Relax-and-Fix Heuristic},
  author =       {Joncour, C. and Kritter, J. and Michel, S. and Schepler, X.},
  journal =      cor,
  volume =       149,
  pages =        106038,
  year =         2023,
  publisher =    {Elsevier}
}

@Article{JonSchWel98ego,
  author =       {Donald R. Jones and Matthias Schonlau and William J. Welch},
  title =        {Efficient Global Optimization of Expensive Black-Box
                  Functions},
  journal =      jgo,
  year =         1998,
  volume =       13,
  number =       4,
  pages =        {455--492},
  keywords =     "EGO",
  alias =        "JonSchWel98go",
  annote =       "Proposed EGO algorithm"
}

@Article{JonSpe1992formal,
  title =        {A formal analysis of the role of multi-point crossover in
                  genetic algorithms},
  author =       DeJong #and# {Spears, William M.},
  journal =      amai,
  volume =       5,
  number =       1,
  pages =        {1--26},
  year =         1992,
  publisher =    {Springer}
}

@Article{JooLeyDec2022knapsack,
  author       = {Jorik Jooken and Pieter Leyman} #and# DeCausmaecker,
  title        = {A new class of hard problem instances for the 0--1 knapsack
                  problem},
  journal      = ejor,
  year         = 2022,
  volume       = 301,
  number       = 3,
  pages        = {841--854}
}

@Article{JooLeyWau2023exploring,
  author       = {Jorik Jooken and Pieter Leyman} #and# {Tony Wauters} #and#
                  DeCausmaecker,
  title        = {Exploring search space trees using an adapted version of
                  {Monte} {Carlo} tree search for combinatorial optimization
                  problems},
  journal      = cor,
  year         = 2023,
  volume       = 150,
  pages        = 106070,
  doi          = {10.1016/j.cor.2022.106070}
}

@Article{JosCle1999:jair,
  author =       {D. E. Joslin and D. P. Clements},
  title =        {Squeaky Wheel Optimization},
  journal =      jair,
  year =         1999,
  volume =       10,
  pages =        {353--373}
}

@Article{Jowitt92,
  author =       Jowitt #and# Germanopoulos,
  title =        "Optimal pump scheduling in water supply networks",
  journal =      jwrpm,
  year =         1992,
  volume =       118,
  number =       4,
  pages =        {406--422},
  note =         notreadyet,
  abstract =     "The electricity cost of pumping accounts for a large
                  part of the total operating cost for water-supply
                  networks. This study presents a method based on
                  linear programming for determining an optimal
                  (minimum cost) schedule of pumping on a 24-hr
                  basis. Both unit and maximum demand electricity
                  charges are considered. Account is taken of the
                  relative efficiencies of the available pumps, the
                  structure of the electricity tariff, the
                  consumer-demand profile, and the hydraulic
                  characteristics and operational constraints of the
                  network. The use of extended-period simulation of
                  the network operation in determining the parameters
                  of the linearized network equations and constraints
                  and in studying the optimized network operation is
                  described. An application of the method to an
                  existing network in the United Kingdom is presented,
                  showing that considerable savings are possible. The
                  method was found to be robust and with low
                  computation-time requirements, and is therefore
                  suitable for real-time implementation.",
}

@Article{JuaFauGras2015orp,
  author =       "Angel A. Juan and Javier Faulin and Scott E. Grasman and
                  Markus Rabe and Gon{\c c}alo Figueira",
  title =        "A review of simheuristics: Extending metaheuristics to deal
                  with stochastic combinatorial optimization problems",
  journal =      orp,
  volume =       2,
  pages =        "62--72",
  year =         2015,
  doi =          "10.1016/j.orp.2015.03.001",
  keywords =     "Metaheuristics; Simulation; Combinatorial optimization;
                  Stochastic problems"
}

@Article{JuaLouMatLuoCas2014,
  author =       {Angel A. Juan} #and# Lourenco_HR #and# {Manuel Mateo and Rachel Luo and Quim Castell{\`{a}}},
  title =        {Using Iterated Local Search for Solving the Flow-shop Problem: Parallelization,
               Parametrization, and Randomization Issues},
  journal =      itor,
  year =         2014,
  volume =       21,
  number =       1,
  pages =        {103--126}
}

@Article{JunReiThi1994,
  author = 	 {M. J{\"u}nger} #and# Reinelt #and# {S. Thienel},
  title = 	 {Provably Good Solutions for the Traveling Salesman Problem},
  journal = 	 zor,
  year = 	 1994,
  volume = 	 40,
  number = 	 2,
  pages = 	 {183--217}
}

@Article{KabColKorLop2017jacryst,
  author =       Kabova #and# Cole_JC #and# Korb_O #and# Lopez-Ibanez #and#
                  Williams_AC #and# Shankland,
  title =        "Improved performance of crystal structure solution from
                  powder diffraction data through parameter tuning of a
                  simulated annealing algorithm",
  journal =      jacryst,
  year =         2017,
  volume =       50,
  number =       5,
  pages =        "1411--1420",
  month =        oct,
  doi =          {10.1107/S1600576717012602},
  abstract =     {Significant gains in the performance of the simulated
                  annealing algorithm in the {\it DASH} software package have
                  been realized by using the {\it irace} automatic
                  configuration tool to optimize the values of three key
                  simulated annealing parameters. Specifically, the success
                  rate in finding the global minimum in intensity $\chi^2$
                  space is improved by up to an order of magnitude. The general
                  applicability of these revised simulated annealing parameters
                  is demonstrated using the crystal structure determinations of
                  over 100 powder diffraction datasets.},
  keywords =     {crystal structure determination, powder diffraction,
                  simulated annealing, parameter tuning, irace},
}

@Article{KahTve1979prospect,
  title =        {Prospect theory: {An} analysis of decision under risk},
  author =       Kahneman #and# Tversky,
  journal =      econometrica,
  pages =        {263--291},
  volume =       47,
  number =       2,
  year =         1979,
  doi =          {10.2307/1914185}
}

@Article{Kahneman2003maps,
  title =        {Maps of bounded rationality: Psychology for behavioral
                  economics},
  author =       Kahneman,
  journal =      amecrev,
  volume =       93,
  number =       5,
  pages =        {1449--1475},
  year =         2003,
}

@Article{KalHasHemSor2023deeprl,
  author       = {Kallestad, Jakob and Hasibi, Ramin and Hemmati, Ahmad} #and#
                  Sorensen,
  title        = {A general deep reinforcement learning hyperheuristic
                  framework for solving combinatorial optimization problems},
  journal      = ejor,
  year         = 2023,
  volume       = 309,
  number       = 1,
  pages        = {446--468},
  month        = aug,
  doi          = {10.1016/j.ejor.2023.01.017},
  keywords     = {Deep RL, hyper-heuristic, ALNS}
}

@Article{KanHeWei2013,
  author       = {Qinma Kang and Hong He and Jun Wei},
  title        = {An Effective Iterated Greedy Algorithm for
                  Reliability-oriented Task Allocation in Distributed Computing
                  Systems},
  journal      = jpdc,
  year         = 2013,
  volume       = 73,
  number       = 8,
  pages        = {1106--1115}
}

@Article{Kar2016,
  author       = {Korhan Karabulut},
  title        = {A hybrid iterated greedy algorithm for total tardiness
                  minimization in permutation flowshops},
  journal      = cie,
  year         = 2016,
  volume       = 98,
  number       = {Supplement C},
  pages        = {300 -- 307}
}

@Article{KarAka2009:airev,
  author =       Karaboga_D #and# Akay_B,
  title =        {A Survey: Algorithms Simulating Bee Swarm Intelligence},
  journal =      airev,
  year =         2009,
  volume =       31,
  number =       {1--4},
  pages =        {61--85}
}

@Article{KarHooEib2015:tec,
  author =       {Giorgos Karafotias and Mark Hoogendoorn} #and# Eiben,
  title =        {Parameter Control in Evolutionary Algorithms: Trends and Challenges},
  journal =      tec,
  year =         2015,
  volume =       19,
  number =       2,
  pages =        {167--187}
}

@Article{KarKok2010tdea,
  title =        {A territory defining multiobjective evolutionary algorithms
                  and preference incorporation},
  author =       Karahan #and# Koksalan,
  journal =      tec,
  volume =       14,
  number =       4,
  pages =        {636--664},
  year =         2010,
  keywords =     "TDEA",
  doi =          {10.1109/TEVC.2009.2033586}
}

@Article{KarMohMey2022ml,
  author       = {Maryam Karimi-Mamaghan and Mehrdad Mohammadi and Patrick
                  Meyer and Amir Mohammad Karimi-Mamaghan} #and# Talbi,
  title        = {Machine learning at the service of meta-heuristics for
                  solving combinatorial optimization problems: A
                  state-of-the-art},
  journal      = ejor,
  year         = 2022,
  volume       = 296,
  number       = 2,
  pages        = {393--422},
  doi          = {10.1016/j.ejor.2021.04.032},
  keywords     = {Meta-heuristics, Machine learning, Combinatorial optimization
                  problems, State-of-the-art},
  abstract     = {In recent years, there has been a growing research interest
                  in integrating machine learning techniques into
                  meta-heuristics for solving combinatorial optimization
                  problems. This integration aims to lead meta-heuristics
                  toward an efficient, effective, and robust search and improve
                  their performance in terms of solution quality, convergence
                  rate, and robustness. Since various integration methods with
                  different purposes have been developed, there is a need to
                  review the recent advances in using machine learning
                  techniques to improve meta-heuristics. To the best of our
                  knowledge, the literature is deprived of having a
                  comprehensive yet technical review. To fill this gap, this
                  paper provides such a review on the use of machine learning
                  techniques in the design of different elements of
                  meta-heuristics for different purposes including algorithm
                  selection, fitness evaluation, initialization, evolution,
                  parameter setting, and cooperation. First, we describe the
                  key concepts and preliminaries of each of these ways of
                  integration. Then, the recent advances in each way of
                  integration are reviewed and classified based on a proposed
                  unified taxonomy. Finally, we provide a technical discussion
                  on the advantages, limitations, requirements, and challenges
                  of implementing each of these integration ways, followed by
                  promising future research directions.}
}

@Article{KarShiDai05:medicine,
  title =        {Prediction of {MHC} class {II} binders using the ant colony
                  search strategy},
  author =       {Karpenko, Oleksiy and Shi, Jianming and Dai, Yang},
  journal =      aimed,
  volume =       35,
  number =       1,
  pages =        {147--156},
  year =         2005,
}

@Article{KarTas2014,
  author =       {Korhan Karabulut and Fatih M. Tasgetiren},
  title =        {A Variable Iterated Greedy Algorithm for the Traveling Salesman Problem with Time Windows},
  journal =      is,
  year =         2014,
  volume =       279,
  pages =        {383--395}
}

@Article{KasNatRee2017ems,
  title =        {Many objective robust decision making for complex
                  environmental systems undergoing change},
  author =       Kasprzyk #and# {Nataraj, Shanthi} #and# Reed_P #and# {Lempert,
                  Robert J.},
  journal =      ems,
  volume =       42,
  pages =        {55--71},
  year =         2013,
  keywords = "scenario-based"
}

@Article{KasReeCha2012ems,
  title =        {Many-objective de {Novo} water supply portfolio planning
                  under deep uncertainty},
  author =       Kasprzyk #and# Reed_P #and# {Characklis, Gregory W. and
                  Kirsch, Brian R.},
  journal =      ems,
  volume =       34,
  pages =        {87--104},
  year =         2012,
  keywords = "scenario-based"
}

@Article{KazCohJea2020,
  author =       {Artem Kaznatcheev and David A. Cohen and Peter Jeavons},
  title =        {Representing Fitness Landscapes by Valued Constraints to Understand
                  the Complexity of Local Search},
  journal =      jair,
  volume =       69,
  pages =        {1077--1102},
  year =         2020,
  doi =          {10.1613/jair.1.12156},
}

@Article{KeArcFen08,
  author =       {Liangjun Ke and Claudia Archetti and Zuren Feng},
  title =        {Ants can solve the team orienteering problem},
  volume =       54,
  number =       3,
  journal =      cie,
  year =         2008,
  pages =        {648--665},
  doi =          {10.1016/j.cie.2007.10.001},
  abstract =     {The team orienteering problem {(TOP)} involves
                  finding a set of paths from the starting point to
                  the ending point such that the total collected
                  reward received from visiting a subset of locations
                  is maximized and the length of each path is
                  restricted by a pre-specified limit. In this paper,
                  an ant colony optimization {(ACO)} approach is
                  proposed for the team orienteering problem. Four
                  methods, i.e., the sequential,
                  deterministic-concurrent and random-concurrent and
                  simultaneous methods, are proposed to construct
                  candidate solutions in the framework of {ACO}. We
                  compare these methods according to the results
                  obtained on well-known problems from the
                  literature. Finally, we compare the algorithm with
                  several existing algorithms. The results show that
                  our algorithm is promising.},
  keywords =     {Ant colony optimization, Ant system, Heuristics,
                  Team orienteering problem},
}

@Article{Kee1981or,
  author =       {R. L. Keeney},
  title =        {Analysis of preference dependencies among objectives},
  journal =      or,
  year =         1981,
  volume =       29,
  pages =        {1105--1120},
}

@Article{KenBaiBla2016good,
  author =       Kendall_G #and# {Ruibin Bai} #and# {Jacek B{\l}azewicz} #and#
                  DeCausmaecker #and# Gendreau #and# {Robert John} #and#
                  {Jiawei Li} #and# McCollum #and# {Erwin Pesch} #and# Qu_R
                  #and# {Nasser Sabar} #and# VandenBerghe #and# {Angelina Yee},
  title =        {Good Laboratory Practice for Optimization Research},
  year =         2016,
  volume =       67,
  number =       4,
  pages =        {676--689},
  journal =      jors,
  doi =          {10.1057/jors.2015.77},
  alias =        "Ken++2016:jors",
}

@Article{KerHooNeuTra2019,
  author =       Kerschke_P #and# Hoos #and# Neumann_F #and# Trautmann,
  title =        {Automated Algorithm Selection: Survey and Perspectives},
  journal =      ec,
  volume =       27,
  number =       1,
  pages =        {3--45},
  year =         2019,
  doi =          {10.1162/evco_a_00242},
  month =        mar,
}

@Article{KerLin70,
  author =       {B. W. Kernighan and S. Lin},
  title =        {An Efficient Heuristic Procedure for Partitioning
                  Graphs},
  journal =      {Bell Systems Technology Journal},
  year =         1970,
  volume =       49,
  number = 	 2,
  pages =        {213--219}
}

@Article{KerTra2019,
  author =       Kerschke_P #and# Trautmann,
  title =        {Automated Algorithm Selection on Continuous Black-Box
                  Problems by Combining Exploratory Landscape Analysis and
                  Machine Learning},
  journal =      ec,
  volume =       27,
  number =       1,
  pages =        {99--127},
  year =         2019,
  doi =          {10.1162/evco_a_00236},
  abstract =     {In this article, we build upon previous work on designing
                  informative and efficient Exploratory Landscape Analysis
                  features for characterizing problems' landscapes and show
                  their effectiveness in automatically constructing algorithm
                  selection models in continuous black-box optimization
                  problems. Focusing on algorithm performance results of the
                  COCO platform of several years, we construct a representative
                  set of high-performing complementary solvers and present an
                  algorithm selection model that, compared to the portfolio's
                  single best solver, on average requires less than half of the
                  resources for solving a given problem. Therefore, there is a
                  huge gain in efficiency compared to classical ensemble
                  methods combined with an increased insight into problem
                  characteristics and algorithm properties by using informative
                  features. The model acts on the assumption that the function
                  set of the Black-Box Optimization Benchmark is representative
                  enough for practical applications. The model allows for
                  selecting the best suited optimization algorithm within the
                  considered set for unseen problems prior to the optimization
                  itself based on a small sample of function evaluations. Note
                  that such a sample can even be reused for the initial
                  population of an evolutionary (optimization) algorithm so
                  that even the feature costs become negligible. }
}

@Article{KerWanPreuGrim2019search,
  doi =          {10.1162/evco_a_00234},
  year =         2019,
  publisher =    mit-pub,
  volume =       27,
  number =       4,
  pages =        {577--609},
  author =       Kerschke_P #and# Wang_H #and# Preuss_M #and# {Christian
                  Grimme} #and# Deutz_AH #and# Trautmann #and#
                  Emmerich,
  title =        {Search Dynamics on Multimodal Multiobjective Problems},
  journal =      ec,
}

@Article{Kerr1998harking,
  doi =          {10.1207/s15327957pspr0203_4},
  year =         1998,
  month =        aug,
  publisher =    {{SAGE} Publications},
  volume =       2,
  number =       3,
  pages =        {196--217},
  author =       {Norbert L. Kerr},
  title =        {{HARKing}: Hypothesizing After the Results are Known},
  journal =      {Personality and Social Psychology Review}
}

@Article{KhuXuHooLey16:aij,
  author =       KhudaBukhsh #and# Xu_Lin #and# Hoos #and# Leyton-Brown,
  title =        {{SATenstein}: Automatically Building Local Search {SAT}
                  {Solvers} from {Components}},
  journal =      aij,
  year =         2016,
  volume =       232,
  pages =        {20--42},
  doi = 	 {10.1016/j.artint.2015.11.002}
}

@Article{KilUrl2015constr,
  author =       {Philip Kilby  and Tommaso Urli},
  title =        {Fleet design optimisation from historical data using constraint programming and large neighbourhood search},
  journal =      constr,
  year =         2015,
  pages =        {1--20},
  publisher =    springer-isorms,
  doi =          {10.1007/s10601-015-9203-0},
  keywords =      {F-race},
}

@Article{Kim1993,
  author =       {Kim, Yeong-Dae},
  title =        {Heuristics for Flowshop Scheduling Problems Minimizing Mean
                  Tardiness},
  journal =      jors,
  year =         1993,
  volume =       44,
  number =       1,
  pages =        {19--28},
  doi =          {10.1057/jors.1993.3},
}

@Article{KimAllLop2020arxiv,
  author =       Kim_Youngmin #and# Allmendinger #and# Lopez-Ibanez,
  title =        {Safe Learning and Optimization Techniques: Towards a Survey
                  of the State of the Art},
  journal =      arxiv # {2101.09505 [cs.LG]},
  year =         2020,
  url =          {https://arxiv.org/abs/2101.09505},
  abstract =     {Safe learning and optimization deals with learning and
                  optimization problems that avoid, as much as possible, the
                  evaluation of non-safe input points, which are solutions,
                  policies, or strategies that cause an irrecoverable loss
                  (e.g., breakage of a machine or equipment, or life
                  threat). Although a comprehensive survey of safe
                  reinforcement learning algorithms was published in 2015, a
                  number of new algorithms have been proposed thereafter, and
                  related works in active learning and in optimization were not
                  considered. This paper reviews those algorithms from a number
                  of domains including reinforcement learning, Gaussian process
                  regression and classification, evolutionary algorithms, and
                  active learning. We provide the fundamental concepts on which
                  the reviewed algorithms are based and a characterization of
                  the individual algorithms. We conclude by explaining how the
                  algorithms are connected and suggestions for future
                  research. },
}

@Article{KimCouYou2021set,
  title =        "Bayesian Optimization with Approximate Set Kernels",
  author =       {Jungtaek Kim and Michael McCourt and Tackgeun You and Saehoon
                  Kim and Seungjin Choi},
  abstract =     {We propose a practical Bayesian optimization method over
                  sets, to minimize a black-box function that takes a set as a
                  single input. Because set inputs are permutation-invariant,
                  traditional Gaussian process-based Bayesian optimization
                  strategies which assume vector inputs can fall short. To
                  address this, we develop a Bayesian optimization method with
                  \emph{set kernel} that is used to build surrogate
                  functions. This kernel accumulates similarity over set
                  elements to enforce permutation-invariance, but this comes at
                  a greater computational cost. To reduce this burden, we
                  propose two key components: (i) a more efficient approximate
                  set kernel which is still positive-definite and is an
                  unbiased estimator of the true set kernel with upper-bounded
                  variance in terms of the number of subsamples, (ii) a
                  constrained acquisition function optimization over sets,
                  which uses symmetry of the feasible region that defines a set
                  input. Finally, we present several numerical experiments
                  which demonstrate that our method outperforms other methods.},
  journal =      ml,
  year =         2021,
  doi = {10.1007/s10994-021-05949-0},
}

@Article{KimParLee2017,
  author =       {Kim, J.-S. and Park, J.-H. and Lee, D.-H.},
  title =        {Iterated Greedy Algorithms to Minimize the Total Family Flow
                  Time for Job-shop Scheduling with Job Families and
                  Sequence-dependent Set-ups},
  journal =      eo,
  year =         2017,
  volume =       49,
  number =       10,
  pages =        {1719--1732}
}

@Article{KinBa2014adam,
  title =        {Adam: A method for stochastic optimization},
  author =       {Kingma, Diederik P. and Ba, Jimmy},
  journal =      arxiv # {1412.6980 [cs.LG]},
  year =         2014,
  url =          {https://arxiv.org/abs/1412.6980},
  annote =       "Published as a conference paper at the 3rd International
                  Conference for Learning Representations, San Diego, 2015~\cite{KinBa2015adam}"
}

@Article{KirTou1985,
  author       = Kirkpatrick #and# {G. Toulouse},
  title        = {Configuration Space Analysis of Travelling Salesman Problems},
  journal      = jdp,
  year         = 1985,
  volume       = 46,
  number       = 8,
  pages        = {1277--1292}
}

@Article{Kirkpatrick1984,
  author       = Kirkpatrick,
  title        = {Optimization by Simulated Annealing: Quantitative Studies},
  journal      = jsp,
  year         = 1984,
  volume       = 34,
  number       = {5-6},
  pages        = {975--986}
}

@Article{Kirkpatrick83,
  author       = Kirkpatrick #and# {C. D. Gelatt} #and# {M. P. Vecchi},
  title        = {Optimization by Simulated Annealing},
  journal      = science,
  year         = 1983,
  volume       = 220,
  number       = 4598,
  pages        = {671--680},
  annote       = {Proposed Simulated Annealing},
  doi          = {10.1126/science.220.4598.671}
}

@Article{KlaMosNau2017iwoven,
  author       = Klamroth #and# Mostaghim_S #and# Naujoks #and# {Silvia
                  Poles}#and# Purshouse_R #and# Rudolph_G #and# {Ruzika,
                  Stefan} #and# {Serpil Say{\i}n} #and# Wiecek_M #and# Yao_Xin,
  title        = {Multiobjective optimization for interwoven systems},
  journal      = jmcda,
  year         = 2017,
  volume       = 24,
  number       = {1-2},
  pages        = {71--81},
  doi          = {10.1002/mcda.1598}
}

@Article{KleShaHom2002,
  author = 	 {Anton J. Kleywegt and Alexander Shapiro and Tito Homem{-}de{-}Mello},
  title = 	 {The Sample Average Approximation Method for Stochastic Discrete Optimization},
  journal = 	 siamo,
  year = 	 2002,
  volume = 	 12,
  number = 	 2,
  pages = 	 {479--502}
}

@Article{Kno2005tec,
  author =       Knowles,
  title =        "{ParEGO}: A hybrid algorithm with on-line landscape
                  approximation for expensive multiobjective optimization
                  problems",
  journal =      tec,
  year =         2006,
  volume =       10,
  number =       1,
  pages =        {50--66},
  doi =          {10.1109/TEVC.2005.851274},
  keywords =     {ParEGO, online, metamodel},
}

@Article{Kno2009closed,
  author =       Knowles,
  title =        "Closed-loop evolutionary multiobjective optimization",
  journal =      cim,
  volume =       4,
  issue =        3,
  pages =        "77--91",
  doi =          "10.1109/MCI.2009.933095",
  year =         2009,
    abstract = {Artificial evolution has been used for more than 50 years as a method of optimization in engineering, operations research and computational intelligence. In closed-loop evolution (a term used by the statistician, George Box) or, equivalently, evolutionary experimentation (Ingo Rechenberg's terminology), the ``phenotypes'' are evaluated in the real world by conducting a physical experiment, whilst selection and breeding is simulated. Well-known early work on artificial evolution\textemdash design engineering problems in fluid dynamics, and chemical plant process optimization\textemdash was carried out in this experimental mode. More recently, the closed-loop approach has been successfully used in much evolvable hardware and evolutionary robotics research, and in some microbiology and biochemistry applications. In this article, several further new targets for closed-loop evolutionary and multiobjective optimization are considered. Four case studies from my own collaborative work are described: (i) instrument optimization in analytical biochemistry; (ii) finding effective drug combinations in vitro; (iii) onchip synthetic biomolecule design; and (iv) improving chocolate production processes. Accurate simulation in these applications is not possible due to complexity or a lack of adequate analytical models. In these and other applications discussed, optimizing experimentally brings with it several challenges: noise; nuisance factors; ephemeral resource constraints; expensive evaluations, and evaluations that must be done in (large) batches. Evolutionary algorithms (EAs) are largely equal to these vagaries, whilst modern multiobjective EAs also enable tradeoffs among conflicting optimization goals to be explored. Nevertheless, principles from other disciplines, such as statistics, Design of Experiments, machine learning and global optimization are also relevant to aspects of the closed-loop problem, and may inspire futher development of multiobjective EAs.},
  langid = {english}
}

@Article{KnoCor00paes,
  author =       Knowles #and# Corne,
  title =        {Approximating the Nondominated Front Using the
                  {Pareto} Archived Evolution Strategy},
  journal =      ec,
  volume =       8,
  number =       2,
  pages =        {149--172},
  year =         2000,
  doi =         {10.1162/106365600568167},
  annote = "Proposed PAES"
}

@Article{KnoCor2003tec,
  author =       Knowles #and# Corne,
  title =        {Properties of an Adaptive Archiving Algorithm for Storing
                  Nondominated Vectors},
  journal =      tec,
  year =         2003,
  volume =       7,
  number =       2,
  pages =        {100--116},
  month =        apr,
  keywords =     "S-metric, hypervolume",
  annote =       "Proposed to use S-metric (hypervolume metric) for
                  environmental selection"
}

@Article{KnoVanGro2011,
  author =       {Knol, Mirjam J. and VanderWeele, Tyler J. and Groenwold, Rolf H. H.
                  and Klungel, Olaf H. and Rovers, Maroeska M. and Grobbee, Diederick E.},
  title =        {Estimating measures of interaction on an additive scale for preventive exposures},
  journal =      eje,
  year =         2011,
  volume =       26,
  number =       6,
  pages =        {433--438}
}

@Article{KocGloAli2004ors,
  author =       Kochenberger#and# Glover_F #and# {Alidaee, Bahram and Rego,
                  Cesar},
  title =        "A unified modeling and solution framework for combinatorial
                  optimization problems",
  journal =      orspek,
  year =         2004,
  volume =       26,
  number =       2,
  pages =        "237--250"
}

@Article{KocHaoGlo2014bqap,
  title =        {The unconstrained binary quadratic programming problem: a
                  survey},
  author =       Kochenberger #and# Hao_JK #and# Glover_F #and# {Lewis, Mark} #and#
                  {L{\"u}, Zhipeng and Wang, Haibo and Wang, Yang},
  journal =      jco,
  volume =       28,
  number =       1,
  pages =        {58--81},
  year =         2014,
  doi = 	 {10.1007/s10878-014-9734-0}
}

@Article{Koe2009jmcda,
  author =       Koksalan,
  title =        {Multiobjective Combinatorial Optimization: Some
                  Approaches},
  journal =      jmcda,
  year =         2009,
  volume =       15,
  pages =        {69--78},
  doi =          {10.1002/mcda.425}
}

@Article{KokKar2010itdea,
  title =        {An Interactive Territory Defining Evolutionary Algorithm:
                  {iTDEA}},
  volume =       14,
  doi =          {10.1109/TEVC.2010.2070070},
  number =       5,
  journal =      tec,
  author =       Koksalan #and# Karahan,
  month =        oct,
  year =         2010,
  pages =        {702--722},
}

@Article{KolHar2006ejor,
  author =       {Kolisch, Rainer and Hartmann, S{\"o}nke},
  title =        {Experimental investigation of heuristics for
                  resource-constrained project scheduling: An update},
  volume =       174,
  doi =          {10.1016/j.ejor.2005.01.065},
  abstract =     {This paper considers heuristics for the well-known
                  resource-constrained project scheduling problem
                  ({RCPSP).} It provides an update of our survey which
                  was published in 2000. We summarize and categorize a
                  large number of heuristics that have recently been
                  proposed in the literature. Most of these heuristics
                  are then evaluated in a computational study and
                  compared on the basis of our standardized
                  experimental design. Based on the computational
                  results we discuss features of good heuristics. The
                  paper closes with some remarks on our test design
                  and a summary of the recent developments in research
                  on heuristics for the {RCPSP}.},
  number =       1,
  journal =      ejor,
  month =        oct,
  year =         2006,
  keywords =     {Computational evaluation, Heuristics, Project
                  scheduling, Resource constraints},
  pages =        {23--37},
}

@Article{KolPap2007approx,
  title =        {Approximately dominating representatives},
  author =       {Koltun, Vladlen} #and# Papadimitriou,
  journal =      tcs,
  year =         2007,
  number =       3,
  pages =        {148--154},
  volume =       371,
  publisher =    {Elsevier}
}

@Article{KolPes1994,
  author =       {A. Kolen} #and# Pesch,
  title =        {Genetic Local Search in Combinatorial Optimization},
  journal =      dam,
  year =         1994,
  volume =       48,
  number =       3,
  pages =        {273--284}
}

@Article{KolRee2007video,
  title =        {A framework for visually interactive decision-making and
                  design using evolutionary multi-objective optimization
                  ({VIDEO})},
  author =       Kollat #and# Reed_P,
  journal =      ems,
  volume =       22,
  number =       12,
  pages =        {1691--1704},
  year =         2007,
  keywords =     "glyph plot"
}

@Article{KooBec57,
  author =       "Tjalling C. Koopmans and Martin J. Beckmann",
  title =        "Assignment Problems and the Location of Economic Activities",
  journal =      econometrica,
  volume =       25,
  pages =        "53--76",
  year =         1957,
  annote =       "Introduced the Quadratic Assignment Problem (QAP)"
}

@Article{Kor1985omega,
  author =       {Kornbluth, Jsh},
  title =        {Sequential multi-criterion decision making},
  doi =          {10.1016/0305-0483(85)90045-3},
  abstract =     {In this paper we consider a simple sequential
                  multicriterion decision making problem in which a
                  decision maker has to accept or reject a series of
                  multi-attributed outcomes. We show that using very
                  simple programming techniques, a great deal of the
                  decision making can be automated. The method might
                  be applicable to situations in which a dealer is
                  having to consider sequential offers in a trading
                  market.},
  number =       6,
  volume =       13,
  journal =      omega,
  year =         1985,
  keywords =     {machine decision making},
  pages =        {569--574},
}

@Article{KorMosWal1990choice,
  author       = Korhonen_P #and# {Moskowitz, Herbert} #and# Wallenius_J,
  title        = {Choice Behavior in Interactive Multiple-Criteria Decision
                  Making},
  journal      = aor,
  year         = 1990,
  volume       = 23,
  number       = 1,
  pages        = {161--179},
  month        = dec,
  doi          = {10.1007/BF02204844},
  abstract     = {Choice behavior in an interactive multiple-criteria decision
                  making environment is examined experimentally. A ``free
                  search'' discrete visual interactive reference direction
                  approach was used on a microcomputer by management students
                  to solve two realistic and relevant multiple-criteria
                  decision problems. The results revealed persistent patterns
                  of intransitive choice behavior, and an unexpectedly rapid
                  degree of convergence of the reference direction approach on
                  a preferred solution. The results can be explained using
                  Tversky' additive utility difference model and
                  Kahneman-Tversky's prospect theory. The implications of the
                  results for the design of interactive multiple-criteria
                  decision procedures are discussed.}
}

@Article{KorPagFal2001,
  title =        {On the ``dimensionality curse'' and the ``self-similarity
                  blessing''},
  author =       {Korn, Flip and Pagel, B.-U. and Faloutsos, Christos},
  journal =      tkde,
  volume =       13,
  number =       1,
  pages =        {96--111},
  year =         2001,
  doi =          "10.1109/69.908983",
  abstract =     {Spatial queries in high-dimensional spaces have been studied
                  extensively. Among them, nearest neighbor queries are
                  important in many settings, including spatial databases (Find
                  the k closest cities) and multimedia databases (Find the k
                  most similar images). Previous analyses have concluded that
                  nearest-neighbor search is hopeless in high dimensions due to
                  the notorious "curse of dimensionality". We show that this
                  may be overpessimistic. We show that what determines the
                  search performance (at least for R-tree-like structures) is
                  the intrinsic dimensionality of the data set and not the
                  dimensionality of the address space (referred to as the
                  embedding dimensionality). The typical (and often implicit)
                  assumption in many previous studies is that the data is
                  uniformly distributed, with independence between
                  attributes. However, real data sets overwhelmingly disobey
                  these assumptions; rather, they typically are skewed and
                  exhibit intrinsic ("fractal") dimensionalities that are much
                  lower than their embedding dimension, e.g. due to subtle
                  dependencies between attributes. We show how the Hausdorff
                  and Correlation fractal dimensions of a data set can yield
                  extremely accurate formulas that can predict the I/O
                  performance to within one standard deviation on multiple real
                  and synthetic data sets.}
}

@Article{KorSilRob04:ml-aco,
  author =       Korosec #and# Silc #and# Robic_B,
  title =        {Solving the mesh-partitioning problem with an
                  ant-colony algorithm},
  journal =      pc,
  year =         2004,
  volume =       30,
  pages =        {785--801}
}

@Article{KorSilWalOor2012linear,
  author       = Korhonen_P #and# {Silvennoinen, Kari} #and# Wallenius_J #and#
                  {{\"O}{\"o}rni, Anssi},
  title        = {Can a linear value function explain choices? {An}
                  experimental study},
  journal      = ejor,
  year         = 2012,
  volume       = 219,
  number       = 2,
  pages        = {360--367},
  month        = jun,
  shorttitle   = {Can a linear value function explain choices?},
  doi          = {10.1016/j.ejor.2011.12.040},
  abstract     = {We investigate in a simple bi-criteria experimental study,
                  whether subjects are consistent with a linear value function
                  while making binary choices. Many inconsistencies appeared in
                  our experiment. However, the impact of inconsistencies on the
                  linearity vs. non-linearity of the value function was
                  minor. Moreover, a linear value function seems to predict
                  choices for bi-criteria problems quite well. This ability to
                  predict is independent of whether the value function is
                  diagnosed linear or not. Inconsistencies in responses did not
                  necessarily change the original diagnosis of the form of the
                  value function. Our findings have implications for the design
                  and development of decision support tools for Multiple
                  Criteria Decision Making problems.},
  language     = {en},
  keywords     = {Binary choices, Inconsistency, Linear value function,
                  Multiple criteria, Weights}
}

@Article{KorStuExn07:si,
  author =       Korb_O #and# Stuetzle #and# Exner,
  title =        {An Ant Colony Optimization Approach to Flexible
                  Protein--Ligand Docking},
  journal =      swarm,
  year =         2007,
  volume =       1,
  number =       2,
  pages =        {115--134}
}

@Article{KorStuExn2009jcim,
  author =       Korb_O #and# Stuetzle #and# Exner,
  title =        {Empirical Scoring Functions for Advanced Protein-Ligand Docking with {PLANTS}},
  journal =      jcim,
  year =         2009,
  volume =       49,
  number =       2,
  pages =        {84--96}
}

@Article{KorStuExn2010jcim,
  author =       Korb_O #and# {Peter Monecke and Gerhard Hessler} #and# Stuetzle #and# Exner,
  title =        {pharm{ACO}phore: Multiple Flexible Ligand Alignment Based on Ant Colony Optimization},
  journal =      jcim,
  year =         2010,
  volume =       50,
  number =       9,
  pages =        {1669--1681}
}

@Article{KorWal1998paretorace,
  author       = Korhonen_P #and# Wallenius_J,
  title        = {A pareto race},
  journal      = nrl,
  year         = 1988,
  volume       = 35,
  number       = 6,
  pages        = {615--623},
  doi          =
                  {10.1002/1520-6750(198812)35:6<615::AID-NAV3220350608>3.0.CO;2-K},
  abstract     = {A dynamic and visual ``free-search'' type of interactive
                  procedure for multiple-objective linear programming is
                  presented. The method enables a decision maker to freely
                  search any part of the efficient frontier by controlling the
                  speed and direction of motion. The objective function values
                  are represented in numeric form and as bar graphs on a
                  display. The method is implemented on an IBM PC/1
                  microcomputer and is illustrated using a multiple-objective
                  linear-programming model for managing disposal of sewage
                  sludge in the New York Bight. Some other applications are
                  also briefly discussed.}
}

@Article{Kot2014:aim,
  author = 	 Kotthoff_L,
  title = 	 {Algorithm Selection for Combinatorial Search Problems: {A} Survey},
  journal = 	 aim,
  year = 	 2014,
  volume = 	 35,
  number = 	 3,
  pages = 	 {48--60}
}

@Article{KotNeuRogWit2012swarm,
  author =       Kotzing_T #and# Neumann_F #and# {R{\"o}glin, Heiko} #and# Witt,
  title =        "Theoretical Analysis of Two {ACO} Approaches for the
                  Traveling Salesman Problem",
  journal =      swarm,
  year =         2012,
  volume =       6,
  number =       1,
  pages =        "1--21",
  abstract =     "Bioinspired algorithms, such as evolutionary algorithms and
                  ant colony optimization, are widely used for different
                  combinatorial optimization problems. These algorithms rely
                  heavily on the use of randomness and are hard to understand
                  from a theoretical point of view. This paper contributes to
                  the theoretical analysis of ant colony optimization and
                  studies this type of algorithm on one of the most prominent
                  combinatorial optimization problems, namely the traveling
                  salesperson problem (TSP). We present a new construction
                  graph and show that it has a stronger local property than one
                  commonly used for constructing solutions of the TSP. The
                  rigorous runtime analysis for two ant colony optimization
                  algorithms, based on these two construction procedures, shows
                  that they lead to good approximation in expected polynomial
                  time on random instances. Furthermore, we point out in which
                  situations our algorithms get trapped in local optima and
                  show where the use of the right amount of heuristic
                  information is provably beneficial.",
  doi =          "10.1007/s11721-011-0059-7",
}

@Article{KotThoHooHutLey2016autoweka,
  title =        {{Auto-WEKA} 2.0: Automatic model selection and hyperparameter
                  optimization in {WEKA}},
  author =       Kotthoff_L #and# {Thornton, Chris} #and# Hoos #and# Hutter
                  #and# Leyton-Brown,
  journal =      jmlr,
  volume =       17,
  pages =        {1--5},
  year =         2016
}

@Article{KowStaMad2009sustainable,
  title =        {Sustainable energy futures: Methodological challenges in
                  combining scenarios and participatory multi-criteria
                  analysis},
  author =       {Kowalski, Katharina and Stagl, Sigrid and Madlener, Reinhard
                  and Omann, Ines},
  journal =      ejor,
  volume =       197,
  number =       3,
  pages =        {1063--1074},
  year =         2009,
  publisher =    {Elsevier}
}

@Article{Kra2010,
  author =       {Oliver Kramer},
  title =        {Iterated Local Search with {Powell}'s Method: A Memetic
                  Algorithm for Continuous Global Optimization},
  journal =      mc,
  year =         2010,
  volume =       2,
  number =       1,
  pages =        {69--83},
  doi =          {10.1007/s12293-010-0032-9},
  alias =        {DBLP:journals/memetic/Kramer10}
}

@Article{KraErdBeh2012sumo,
  title =        {Recent development and applications of {SUMO} - {Simulation}
                  of {Urban} {MO}bility},
  author =       Krajzewicz_D #and# {Erdmann, Jakob and Behrisch, Michael
                  and Bieker, Laura},
  journal =      {International Journal On Advances in Systems and
                  Measurements},
  volume =       5,
  number =       {3-4},
  year =         2012,
  pages =        {128--138}
}

@Article{Kreipl00:js,
  author =       {S. Kreipl},
  title =        {A Large Step Random Walk for Minimizing Total Weighted Tardiness in a Job Shop},
  journal =      jos,
  year =         2000,
  volume =       3,
  number =       3,
  pages =        {125--138}
}

@Article{KriTriDoe2017,
  author = 	 {Stefanie Kritzinger and Fabien Tricoire} #and# Doerner #and# Hartl #and# Stuetzle,
  title = 	 {A Unified Framework for Routing Problems with a Fixed Fleet Size},
  journal = 	 ijmh,
  year = 	 2017,
  volume = 	 6,
  number = 	 3,
  pages = 	 {160--209}
}

@Article{Kruskal1956,
  title =        {On the shortest spanning subtree of a graph and the traveling salesman problem},
  author =       {Kruskal, Joseph B},
  journal =      pams,
  volume =       7,
  number =       1,
  pages =        {48--50},
  year =         1956
}

@Article{KuhBie2016:cor,
  author =       {Kuhpfahl, J and Christian Bierwirth},
  title =        {A Study on Local Search Neighborhoods for the Job Shop Scheduling Problem with Total Weighted Tardiness Objective},
  journal =      cor,
  year =         2016,
  volume =       66,
  pages =        {44--57}
}

@Article{KuhFonPaqRuz2016hvsubset,
  title =        {Hypervolume subset selection in two dimensions: Formulations
                  and algorithms},
  author =       {Kuhn, Tobias} #and# Fonseca #and# Paquete #and# {Ruzika,
                  Stefan and Duarte, Miguel M.} #and# Figueira,
  journal =      ec,
  year =         2016,
  number =       3,
  pages =        {411--425},
  volume =       24,
}

@Article{Kuhn1955,
  title =        {The hungarian method for the assignment problem},
  author =       {Kuhn, Harold W.},
  journal =      nrlq,
  volume =       2,
  number =       {1--2},
  pages =        {83--97},
  year =         1955
}

@Article{Kuhn2008:jss,
  author =       {Kuhn, Max},
  title =        {Building Predictive Models in \proglang{R} Using the {\rpackage{caret}} Package},
  journal =      jss,
  volume =       28,
  number =       5,
  year =         2008,
  pages =        {1--26}
}

@Article{KumSin2007sci,
  author =       {Kumar, R. and Singh, P. K.},
  title =        "{Pareto} Evolutionary Algorithm Hybridized with
                  Local Search for Biobjective {TSP}",
  journal =      sci,
  year =         2007,
  volume =       75,
  pages =        {361--398}
}

@Article{KunLucPre1975jacm,
  author =       {H. T. Kung and F. Luccio and F. P. Preparata},
  title =        "On Finding the Maxima of a Set of Vectors",
  pages =        "469--476",
  journal =      jacm,
  year =         1975,
  volume =       22,
  number =       4,
  alias =        {kung:maxima},
}

@Article{KurDav1982ms,
  author =       {Kurtulus, I. and Davis, E. W.},
  title =        {Multi-Project Scheduling: Categorization of
                  Heuristic Rules Performance},
  volume =       28,
  doi =          {10.1287/mnsc.28.2.161},
  abstract =     {Application of heuristic solution procedures to the
                  practical problem of project scheduling has
                  previously been studied by numerous
                  researchers. However, there is little consensus
                  about their findings, and the practicing manager is
                  currently at a loss as to which scheduling rule to
                  use. Furthermore, since no categorization process
                  was developed, it is assumed that once a rule is
                  selected it must be used throughout the whole
                  project. This research breaks away from this
                  tradition by providing a categorization process
                  based on two powerful project summary measures. The
                  first measure identifies the location of the peak of
                  total resource requirements and the second measure
                  identifies the rate of utilization of each resource
                  type. The performance of the rules are classified
                  according to values of these two measures, and it is
                  shown that a rule introduced by this research
                  performs significantly better on most categories of
                  projects.},
  number =       2,
  journal =      ms,
  year =         1982,
  keywords =     {project management, research and development},
  pages =        {161--172},
}

@Article{Kushner1964,
  author =       {Kushner, H. J.},
  journal =      jbe,
  title =        {A New Method of Locating the Maximum Point of an Arbitrary
                  Multipeak Curve in the Presence of Noise},
  year =         1964,
  issn =         {0021-9223},
  month =        mar,
  number =       1,
  pages =        {97--106},
  volume =       86,
  abstract =     {A versatile and practical method of searching a parameter
                  space is presented. Theoretical and experimental results
                  illustrate the usefulness of the method for such problems as
                  the experimental optimization of the performance of a system
                  with a very general multipeak performance function when the
                  only available information is noise-distributed samples of
                  the function. At present, its usefulness is restricted to
                  optimization with respect to one system parameter. The
                  observations are taken sequentially; but, as opposed to the
                  gradient method, the observation may be located anywhere on
                  the parameter interval. A sequence of estimates of the
                  location of the curve maximum is generated. The location of
                  the next observation may be interpreted as the location of
                  the most likely competitor (with the current best estimate)
                  for the location of the curve maximum. A Brownian motion
                  stochastic process is selected as a model for the unknown
                  function, and the observations are interpreted with respect
                  to the model. The model gives the results a simple intuitive
                  interpretation and allows the use of simple but efficient
                  sampling procedures. The resulting process possesses some
                  powerful convergence properties in the presence of noise; it
                  is nonparametric and, despite its generality, is efficient in
                  the use of observations. The approach seems quite promising
                  as a solution to many of the problems of experimental system
                  optimization.},
  doi =          {10.1115/1.3653121},
  epub = "https://asmedigitalcollection.asme.org/fluidsengineering/article-pdf/86/1/97/5763745/97_1.pdf",
}

@Article{Kwa2017ems,
  title =        {The Exploratory Modeling Workbench: An open source toolkit
                  for exploratory modeling, scenario discovery, and
                  (multi-objective) robust decision making},
  author =       Kwakkel,
  journal =      ems,
  volume =       96,
  pages =        {239--250},
  year =         2017,
}

@Article{LTDZ2002b,
  author =       Laumanns #and# Thiele #and# Deb #and# Zitzler,
  title =        {Combining Convergence and Diversity in Evolutionary
                  Multiobjective Optimization},
  journal =      ec,
  year =         2002,
  volume =       10,
  number =       3,
  pages =        "263--282",
  doi =          "10.1162/106365602760234108",
  keywords =     "archiving, $\epsilon$-dominance, $\epsilon$-approximation,
                  $\epsilon$-Pareto",
  annote =       "Proposed $\epsilon$-approx and $\epsilon$-Pareto archivers"
}

@Article{LaTMuePen11:soco,
  author       = {LaTorre, Antonio and Muelas, Santiago and Pe{\~n}a,
                  Jos{\'e}-Mar{\'i}a},
  title        = {A {MOS}-based dynamic memetic differential evolution
                  algorithm for continuous optimization: a scalability test},
  journal      = soco,
  year         = 2011,
  volume       = 15,
  number       = 11,
  pages        = {2187--2199}
}

@Article{LaaAarLen1992:or,
  author =       VanLaarhoven_PJM #and# Aarts #and# Lenstra,
  title =        {Job Shop Scheduling by Simulated Annealing},
  journal =      or,
  year =         1992,
  volume =       40,
  number =       1,
  pages =        {113--125}
}

@Article{LabMarSav1998:npp,
  author    = {Martine Labb{\'e} and Patrice Marcotte and Gilles Savard},
  title     = {A Bilevel Model of Taxation and Its Application to Optimal Highway Pricing},
  journal   = ms,
  volume    = 44,
  number    = 12,
  year      = 1998,
  pages     = {1608--1622},
  doi       = {10.1287/mnsc.44.12.1608},
  publisher = informs
}

@Article{LabVio2013:npp,
  author =       {Martine Labb{\'e} and Alessia Violin},
  title =        {Bilevel programming and price setting problems},
  journal =      j4or,
  year =         2013,
  volume =       11,
  number =       1,
  pages =        {1--30},
  doi =          {10.1007/s10288-012-0213-0}
}

@Article{LacMolHer2014is,
  author       = {Benjamin Lacroix} #and# Molina_D #and# Herrera_F,
  title        = {Region based memetic algorithm for real-parameter
                  optimisation},
  journal      = is,
  year         = 2014,
  volume       = 262,
  pages        = {15--31},
  doi          = {10.1016/j.ins.2013.11.032},
  keywords     = {irace}
}

@Article{Laguna2016:editornote,
  author =       Laguna,
  title =        "Editor's Note on the {MIC} 2013 Special Issue of the
                  Journal of Heuristics (Volume 22, Issue 4, August 2016)",
  journal =      joh,
  year =         2016,
  volume =       22,
  number =       5,
  pages =        "665--666"
}

@Article{LaiHao2016,
  author =       {Xiangjing Lai} #and# Hao_JK,
  title =        {Iterated Maxima Search for the Maximally Diverse Grouping Problem},
  journal =      ejor,
  year =         2016,
  volume =       254,
  number =       3,
  pages =        {780--800}
}

@Article{LanDoi1960,
  author =       {A. H. Land and A. G. Doig},
  journal =      econometrica,
  number =       3,
  pages =        {497--520},
  publisher =    {Wiley, Econometric Society},
  title =        {An Automatic Method of Solving Discrete Programming Problems},
  volume =       28,
  year =         1960
}

@Article{LanHar2015,
  author = 	 Langdon_WB #and# {Mark Harman},
  title = 	 {Optimising Software with Genetic Programming},
  journal = 	 tec,
  year = 	 2015,
  volume = 	 19,
  number = 	 1,
  pages = 	 {118--135}
}

@Article{LanKotMar2014jscs,
  author =       {M. Lang and H. Kotthaus and P. Marwedel and C. Weihs and
                  J. Rahnenf{\"u}hrer} #and# Bischl_B,
  title =        {Automatic Model Selection for High-Dimensional Survival
                  Analysis},
  journal =      jscs,
  year =         2014,
  volume =       85,
  number =       1,
  pages =        {62--76},
  doi =          {10.1080/00949655.2014.929131},
}

@Article{LanSouDes1990,
  title =        {Classification of travelling salesman problem formulations},
  author =       Langevin #and# Soumis #and# Desrosiers,
  journal =      orl,
  volume =       9,
  number =       2,
  pages =        {127--132},
  year =         1990,
  publisher =    elsevier
}

@Article{Langevin93tsptw,
  author =       Langevin #and# Desrochers #and# Desrosiers #and# Gelinas_S
                  #and# Soumis,
  title =        {A Two-Commodity Flow Formulation for the Traveling Salesman
                  and Makespan Problems with Time Windows},
  journal =      networks,
  year =         1993,
  volume =       23,
  number =       7,
  pages =        {631--640},
}

@Article{Lansey94,
  author =       Lansey #and# Awumah,
  title =        "Optimal Pump Operations Considering Pump Switches",
  journal =      jwrpm,
  volume =       120,
  number =       1,
  pages =        {17--35},
  date =         "1994-01/1994-02",
  year =         1994,
  month =        jan #" / "# feb,
}

@Article{Laporte2009,
  author = 	 Laporte,
  title = 	 {Fifty Years of Vehicle Routing},
  journal = 	 ts,
  year = 	 2009,
  volume = 	 43,
  number = 	 4,
  pages = 	 {408--416}
}

@Article{Lau2007arxiv,
  title =        {Stochastic convergence of random search to fixed size
                  {Pareto} set approximations},
  author =       Laumanns,
  journal =      arxiv # {0711.2949},
  year =         2007,
  alias =        {laumanns2007stochastic},
  url =          {https://arxiv.org/abs/0711.2949}
}

@Article{LauHao2009:cie,
  author =       {Beno{\^i}t Laurent} #and# Hao_JK,
  title =        {Iterated Local Search for the Multiple Depot Vehicle Scheduling Problem},
  journal =      cie,
  year =         2009,
  volume =       57,
  number =       1,
  pages =        {277--286}
}

@Article{LauThiZit2004,
  title =        {Running time analysis of multiobjective evolutionary
                  algorithms on pseudo-boolean functions},
  author =       Laumanns #and# Thiele #and# Zitzler,
  journal =      tec,
  volume =       8,
  number =       2,
  pages =        {170--182},
  year =         2004,
}

@Article{LauThiZit2004nc,
  author =       Laumanns #and# Thiele #and# Zitzler,
  title =        {Running time analysis of evolutionary algorithms on a
                  simplified multiobjective knapsack problem},
  volume =       3,
  journal =      natcomp,
  number =       1,
  pages =        {37--51},
  year =         2004
}

@Article{LauZen2011ejor,
  author =       Laumanns #and# {Zenklusen, Rico},
  title =        {Stochastic convergence of random search methods to
                  fixed size {Pareto} front approximations},
  journal =      ejor,
  volume =       213,
  number =       2,
  pages =        {414--421},
  year =         2011,
  doi =          {10.1016/j.ejor.2011.03.039},
}

@Article{LawWoo1966,
  author =       {Lawler, E. L. and Wood, D. E.},
  title =        {Branch-and-Bound Methods: A Survey},
  journal =      or,
  volume =       14,
  number =       4,
  year =         1966,
  pages =        {699--719},
  numpages =     21,
  doi =          {10.1287/opre.14.4.699},
  publisher =    informs,
}

@Article{Lazic2004,
  author =       {S. E. Lazic},
  title =        {The problem of pseudoreplication in neuroscientific studies:
                  is it affecting your analysis?},
  journal =      {{BMC} Neuroscience},
  volume =       11,
  number =       5,
  pages =        {397--407},
  year =         2004,
  doi =          {10.1186/1471-2202-11-5}
}

@Article{LeCBen1995convnet,
  title =        {Convolutional networks for images, speech, and time series},
  author =       {LeCun, Yann} #and# Bengio_Y # and_others,
  journal =      {The handbook of brain theory and neural networks},
  volume =       3361,
  number =       10,
  pages =        "255--258",
  year =         1995
}

@Article{LeCBenHin2015nature,
  title =        {Deep learning},
  author =       {LeCun, Yann} #and# Bengio_Y #and# {Hinton, Geoffrey},
  journal =      nature,
  volume =       521,
  number =       7553,
  pages =        {436--444},
  year =         2015,
  publisher =    {Nature Research}
}

@Article{LeaTavMouMac2016:itor,
  author =       {Vin{\'i}cius {Leal do Forte} and Fl{\'a}vio Marcelo {Tavares
                  Montenegro} and Jos{\'e} Andr{\'e} {de Moura Brito} and
                  Nelson Maculan},
  title =        {Iterated Local Search Algorithms for the {Euclidean}
                  {Steiner} Tree Problem in \emph{n} Dimensions},
  journal =      itor,
  year =         2016,
  volume =       23,
  number =       6,
  pages =        {1185--1199}
}

@Article{LehWit2012,
  title =        {Black-box search by unbiased variation},
  author =       {Lehre, Per Kristian} #and# Witt,
  journal =      algorithmica,
  volume =       64,
  number =       4,
  pages =        {623--642},
  year =         2012,
  publisher =    springer
}

@Article{Leighton1979,
  author =       {Frank Thomson Leighton},
  title =        {A Graph Coloring Algorithm for Large Scheduling Problems},
  journal =      jornbs,
  year =         1979,
  volume =       84,
  number =       6,
  pages =        {489--506}
}

@Article{LemGroPop2006general,
  title =        {A general analytic method for generating robust strategies
                  and narrative scenarios},
  author =       {Lempert, Robert J. and Groves, David G. and Popper, Steven
                  W.} #and# Bankes_S,
  journal =      ms,
  volume =       52,
  number =       4,
  pages =        {514--528},
  year =         2006,
  publisher =    informs
}

@Article{Leon00,
  author =       {C. Leon and S. Martin and J. M. Elena and J. Luque},
  title =        {{EXPLORE}: Hybrid expert system for water networks
                  management},
  journal =      jwrpm,
  year =         2000,
  volume =       126,
  number =       2,
  pages =        {65--74},
}

@Article{Levin1973,
  title =        {Universal'nyie perebornyie zadachi},
  author =       {Levin, Leonid},
  journal =      {Problemy Peredachi Informatsii},
  volume =       9,
  pages =        {265--266},
  year =         1973
}

@Article{LewKurJoe2009gen,
  author       = {Lewandowski, Daniel and Kurowicka, Dorota and Joe, Harry},
  title        = {Generating Random Correlation Matrices Based on Vines and
                  Extended Onion Method},
  journal      = jmva,
  year         = 2009,
  volume       = 100,
  number       = 9,
  pages        = {1989--2001},
  doi          = {10.1016/j.jmva.2009.04.008},
  abstract     = {We extend and improve two existing methods of generating
                  random correlation matrices, the onion method of Ghosh and
                  Henderson [S. Ghosh, S.G. Henderson, Behavior of the norta
                  method for correlated random vector generation as the
                  dimension increases, ACM Transactions on Modeling and
                  Computer Simulation (TOMACS) 13 (3) (2003) 276-294] and the
                  recently proposed method of Joe [H. Joe, Generating random
                  correlation matrices based on partial correlations, Journal
                  of Multivariate Analysis 97 (2006) 2177-2189] based on
                  partial correlations. The latter is based on the so-called
                  D-vine. We extend the methodology to any regular vine and
                  study the relationship between the multiple correlation and
                  partial correlations on a regular vine. We explain the onion
                  method in terms of elliptical distributions and extend it to
                  allow generating random correlation matrices from the same
                  joint distribution as the vine method. The methods are
                  compared in terms of time necessary to generate 5000 random
                  correlation matrices of given dimensions.},
  keywords     = {Correlation matrix; Dependence vines; Onion method; Partial
                  correlation; LKJ}
}

@Article{Li2008two,
  title =        {A two-step rejection procedure for testing multiple
                  hypotheses},
  author =       {Li, Jianjun David},
  journal =      jspi,
  volume =       138,
  number =       6,
  pages =        {1521--1527},
  year =         2008,
}

@Article{Li2021telo,
  title =        {Is Our Archiving Reliable? Multiobjective Archiving Methods
                  on ``Simple'' Artificial Input Sequences},
  author =       Li_Miqing,
  journal =      telo,
  year =         2021,
  number =       3,
  pages =        {1--19},
  volume =       1,
  doi =          "10.1145/3465335",
}

@Article{LiCheFuYao2018twoarch,
  title =        {Two-archive evolutionary algorithm for constrained
                  multiobjective optimization},
  author =       {Li, Ke and Chen, Renzhi and Fu, Guangtao} #and# Yao_Xin,
  journal =      tec,
  year =         2018,
  number =       2,
  pages =        {303--315},
  volume =       23,
  publisher =    {IEEE}
}

@Article{LiCheYao2020ieeese,
  author       = Li_Miqing #and# {Chen, Tao} #and# Yao_Xin,
  title        = {How to evaluate solutions in {Pareto}-based search-based
                  software engineering? {A} critical review and methodological
                  guidance},
  journal      = ieeese,
  year         = 2020,
  volume       = 48,
  number       = 5,
  pages        = {1771--1799},
  doi          = {10.1109/TSE.2020.3036108}
}

@Article{LiGroYanLiu2018multi,
  title =        {Multi-line distance minimization: A visualized many-objective
                  test problem suite},
  author =       Li_Miqing #and# {Grosan, Crina} #and# {Yang, Shengxiang and
                  Liu, Xiaohui} #and# Yao_Xin,
  journal =      tec,
  year =         2018,
  number =       1,
  pages =        {61--78},
  volume =       22,
  annote =       "highly degenerate Pareto fronts"
}

@Article{LiLopYao2023archiving,
  author       = Li_Miqing #and# Lopez-Ibanez #and# Yao_Xin,
  title        = {Multi-Objective Archiving},
  journal      = tec,
  year         = 2023,
  volume       = 28,
  number       = 3,
  pages        = {696--717},
  doi          = {10.1109/TEVC.2023.3314152},
  abstract     = {Most multi-objective optimisation algorithms maintain an
                  archive explicitly or implicitly during their search. Such an
                  archive can be solely used to store high-quality solutions
                  presented to the decision maker, but in many cases may
                  participate in the search process (e.g., as the population in
                  evolutionary computation). Over the last two decades,
                  archiving, the process of comparing new solutions with
                  previous ones and deciding how to update the
                  archive/population, stands as an important issue in
                  evolutionary multi-objective optimisation (EMO). This is
                  evidenced by constant efforts from the community on
                  developing various effective archiving methods, ranging from
                  conventional Pareto-based methods to more recent
                  indicator-based and decomposition-based ones. However, the
                  focus of these efforts is on empirical performance comparison
                  in terms of specific quality indicators; there is lack of
                  systematic study of archiving methods from a general
                  theoretical perspective. In this paper, we attempt to conduct
                  a systematic overview of multi-objective archiving, in the
                  hope of paving the way to understand archiving algorithms
                  from a holistic perspective of theory and practice, and more
                  importantly providing a guidance on how to design
                  theoretically desirable and practically useful archiving
                  algorithms. In doing so, we also present that archiving
                  algorithms based on weakly Pareto compliant indicators (e.g.,
                  $\epsilon$-indicator), as long as designed properly, can
                  achieve the same theoretical desirables as archivers based on
                  Pareto compliant indicators (e.g., hypervolume
                  indicator). Such desirables include the property
                  limit-optimal, the limit form of the possible optimal
                  property that a bounded archiving algorithm can have with
                  respect to the most general form of superiority between
                  solution sets.}
}

@Article{LiShaBah2016traffic,
  author =       {Li, Zhiyi and Shahidehpour, Mohammad and Bahramirad, Shay and
                  Khodaei, Amin},
  doi =          {10.1109/TSG.2016.2526032},
  alias =        "Li2016",
  journal =      ieeetsg,
  number =       4,
  pages =        {1--1},
  title =        {Optimizing Traffic Signal Settings in Smart Cities},
  volume =       3053,
  year =         2016,
  abstract =     {Traffic signals play a critical role in smart cities for
                  mitigating traffic congestions and reducing the emission in
                  metropolitan areas. This paper proposes a bi-level
                  optimization framework to settle the optimal traffic signal
                  setting problem. The upper-level problem determines the
                  traffic signal settings to minimize the drivers' average
                  travel time, while the lower-level problem aims for achieving
                  the network equilibrium using the settings calculated at the
                  upper level. Genetic algorithm is employed with the
                  integration of microscopic-traffic-simulation based dynamic
                  traffic assignment (DTA) to decouple the complex bi-level
                  problem into tractable single-level problems which are solved
                  sequentially. Case studies on a synthetic traffic network and
                  a real-world traffic subnetwork are conducted to examine the
                  effectiveness of the proposed model and relevant solution
                  methods. Additional strategies are provided for the extension
                  of the proposed model and the acceleration solution process
                  in large-area traffic network applications.},
}

@Article{LiChenXuGupta2015,
  author =    {Xiaoping Li and Long Chen and Haiyan Xu and Jatinder N. D. Gupta},
  title =     {Trajectory Scheduling Methods for Minimizing Total Tardiness in a Flowshop},
  journal =   orp,
  volume =    2,
  pages =     {13--23},
  year =      2015,
  issn =      {2214--7160},
  doi =       {10.1016/j.orp.2014.12.001},
}

@Article{LiJamSal2018hyperband,
  author =       {Lisha Li and Kevin Jamieson and Giulia DeSalvo and Afshin
                  Rostamizadeh and Ameet Talwalkar},
  title =        {Hyperband: A Novel Bandit-Based Approach to Hyperparameter
                  Optimization},
  journal =      jmlr,
  year =         2018,
  volume =       18,
  number =       185,
  pages =        {1--52},
  epub =          {http://jmlr.org/papers/v18/16-558.html},
  abstract = {Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation and early-stopping. We formulate hyperparameter optimization as a pure-exploration non-stochastic infinite-armed bandit problem where a predefined resource like iterations, data samples, or features is allocated to randomly sampled configurations. We introduce a novel algorithm, our algorithm, for this framework and analyze its theoretical properties, providing several desirable guarantees. Furthermore, we compare our algorithm with popular Bayesian optimization methods on a suite of hyperparameter optimization problems. We observe that our algorithm can provide over an order-of-magnitude speedup over our competitor set on a variety of deep-learning and kernel-based learning problems.},
  keywords = {racing}
}

@Article{LiLi07,
  author =       {Y. Li and W. Li},
  title =        {Adaptive Ant Colony Optimization Algorithm Based on
                  Information Entropy: Foundation and Application},
  journal =      {Fundamenta Informaticae},
  volume =       77,
  number =       3,
  year =         2007,
  pages =        {229--242},
  publisher =    ios-pub,
  address =      ios-ad,
}

@Article{LiLiTanYao2015many,
  author =       {Li, Bingdong and Li, Jinlong and Tang, Ke} #and# Yao_Xin,
  title =        {Many-Objective Evolutionary Algorithms: A Survey},
  journal =      acm-cs,
  volume =       48,
  number =       1,
  year =         2015,
  pages =        {1--35},
  doi =          {10.1145/2792984},
  numpages =     35,
}

@Article{LiTanLiYao2016stochastic,
  title =        {Stochastic ranking algorithm for many-objective optimization
                  based on multiple indicators},
  author =       {Li, Bingdong and Tang, Ke and Li, Jinlong} #and# Yao_Xin,
  journal =      tec,
  year =         2016,
  number =       6,
  pages =        {924--938},
  volume =       20,
  publisher =    {IEEE}
}

@Article{LiYanLiu2014shift,
  title =        {Shift-based density estimation for {Pareto}-based algorithms
                  in many-objective optimization},
  author =       Li_Miqing #and# {Yang, Shengxiang and Liu, Xiaohui},
  journal =      tec,
  year =         2014,
  number =       3,
  pages =        {348--365},
  volume =       18,
  publisher =    {IEEE},
  annote =       "Proposed SDE indicator algorithm"
}

@Article{LiYanLiu2016tec,
  title =        {{Pareto} or non-{Pareto}: {Bi}-criterion evolution in
                  multiobjective optimization},
  author =       Li_Miqing #and# {Yang, Shengxiang and Liu, Xiaohui},
  journal =      tec,
  year =         2016,
  number =       5,
  pages =        {645--665},
  volume =       20,
}

@Article{LiYao2019qual,
  title =        {Quality Evaluation of Solution Sets in Multiobjective
                  Optimisation: A Survey},
  author =       Li_Miqing #and# Yao_Xin,
  journal =      acm-cs,
  year =         2019,
  number =       2,
  volume =       52,
  pages =        "1--38",
  doi =          "10.1145/3300148",
  publisher =    {ACM}
}

@Article{LiYao2017arxiv,
  title =        {Dominance Move: A Measure of Comparing Solution Sets in
                  Multiobjective Optimization},
  author =       Li_Miqing #and# Yao_Xin,
  journal =      {arXiv preprint arXiv:1702.00477},
  year =         2017
}

@Article{LiYao2020ec,
  title =        {What weights work for you? Adapting weights for any {Pareto}
                  front shape in decomposition-based evolutionary
                  multiobjective optimisation},
  author =       Li_Miqing #and# Yao_Xin,
  journal =      ec,
  year =         2020,
  number =       2,
  pages =        {227--253},
  volume =       28,
}

@Article{LiZha2009:moead-de,
  title =        {Multiobjective Optimization Problems with Complicated
                  {Pareto} sets, {MOEA/D} and {NSGA-II}},
  author =       {Li, Hui} #and# Zhang_Qingfu,
  journal =      tec,
  volume =       13,
  number =       2,
  pages =        {284--302},
  year =         2009
}

@Article{LiZouYan2021twoarch,
  title =        {A two-archive algorithm with decomposition and fitness
                  allocation for multi-modal multi-objective optimization},
  author =       {Li, Zhipan and Zou, Juan and Yang, Shengxiang and Zheng,
                  Jinhua},
  journal =      is,
  year =         2021,
  pages =        {413--430},
  volume =       574,
  publisher =    {Elsevier}
}

@Article{LiaAydStu13,
  author =       Liao_Tianjun #and# Dogan #and# Stuetzle,
  title =        {Artificial Bee Colonies for Continuous Optimization: Experimental Analysis and Improvements},
  journal =      swarm,
  year =         2013,
  volume =       7,
  number =       4,
  pages =        {327--356}
}

@Article{LiaMolMonStu2014,
  author =       Liao_Tianjun #and# Molina_D #and# MontesDeOca #and# Stuetzle,
  title =        {A Note on the Effects of Enforcing Bound Constraints on
Algorithm Comparisons using the {IEEE} {CEC'05} Benchmark Function Suite},
  journal =      ec,
  year =         2014,
  volume =       22,
  number =       2,
  pages =        {351--359},
}

@Article{LiaMolStu2015,
  author =       Liao_Tianjun #and# Molina_D #and# Stuetzle,
  title =        {Performance Evaluation of Automatically Tuned Continuous
  Optimizers on Different Benchmark Sets},
  journal =      asoco,
  year =         2015,
  volume =       27,
  pages =        {490--503}
}

@Article{LiaMonStu13:soco,
  author =       Liao_Tianjun #and# MontesDeOca #and# Stuetzle,
  title =        {Computational results for an automatically tuned {CMA-ES}
                  with increasing population size on the {CEC'05} benchmark
                  set},
  journal =      soco,
  pages =        {1031--1046},
  volume =       17,
  number =       6,
  year =         2013,
  doi =          {0.1007/s00500-012-0946-x}
}

@Article{LiaSocMonStuDor2014,
  author =       Liao_Tianjun #and# Socha #and# MontesDeOca #and# Stuetzle #and# Dorigo,
  title =        {Ant Colony Optimization for Mixed-Variable Optimization
Problems},
  journal =      tec,
  year =         2014,
  volume =       18,
  number =       4,
  pages =        {503--518},
  keywords = "ACOR",
}

@Article{LiaStuMonDor2014,
  author =       Liao_Tianjun #and# Stuetzle #and# MontesDeOca #and# Dorigo,
  title =        {A Unified Ant Colony Optimization Algorithm for Continuous
Optimization},
  journal =      ejor,
  year =         2014,
  volume =       234,
  number =       3,
  pages =        {597--609}
}

@Article{LiaTseLua07,
  author =       Liao_CJ #and# Tseng_CT #and# Luarn,
  title =        "A Discrete Version of Particle Swarm Optimization
                  for Flowshop Scheduling Problems",
  journal =      cor,
  volume =       34,
  number =       10,
  pages =        "3099--3111",
  year =         2007,
}

@Article{LieDaoVerDer2019tec,
  author =       Liefooghe #and# {Fabio Daolio} #and# Derbel_B #and# Verel
                  #and# Aguirre #and# Tanaka,
  journal =      tec,
  number =       6,
  pages =        {1063--1077},
  title =        {Landscape-Aware Performance Prediction for Evolutionary
                  Multi-objective Optimization},
  volume =       24,
  year =         2020
}

@Article{LieHumMes2011,
  author =       Liefooghe #and# Humeau #and# Mesmoudi #and# Jourdan #and#
                  Talbi,
  title =        "On dominance-based multiobjective local search: design,
                  implementation and experimental analysis on scheduling and
                  traveling salesman problems",
  journal =      joh,
  volume =       18,
  number =       2,
  pages =        {317--352},
  year =         2012,
  abstract =     "This paper discusses simple local search approaches for
                  approximating the efficient set of multiobjective
                  combinatorial optimization problems. We focus on algorithms
                  defined by a neighborhood structure and a dominance relation
                  that iteratively improve an archive of nondominated
                  solutions. Such methods are referred to as dominance-based
                  multiobjective local search. We first provide a concise
                  overview of existing algorithms, and we propose a model
                  trying to unify them through a fine-grained
                  decomposition. The main problem-independent search components
                  of dominance relation, solution selection, neighborhood
                  exploration and archiving are largely discussed. Then, a
                  number of state-of-the-art and original strategies are
                  experimented on solving a permutation flowshop scheduling
                  problem and a traveling salesman problem, both on a two- and
                  a three-objective formulation. Experimental results and a
                  statistical comparison are reported in the paper, and some
                  directions for future research are highlighted.",
  doi =          {10.1007/s10732-011-9181-3}
}

@Article{LieJouTal2011paradiseo,
  title =        {A Software Framework Based on a Conceptual Unified
                  Model for Evolutionary Multiobjective Optimization:
                  {ParadisEO}-{MOEO}},
  author =       Liefooghe #and# Jourdan #and# Talbi,
  journal =      ejor,
  volume =       209,
  number =       2,
  pages =        {104--112},
  year =         2011,
}

@Article{LieVerHao2014hybrid,
  author       = Liefooghe #and# Verel #and# Hao_JK,
  title        = {A hybrid metaheuristic for multiobjective unconstrained
                  binary quadratic programming},
  journal      = asoco,
  year         = 2014,
  volume       = 16,
  pages        = {10--19},
  publisher    = {Elsevier}
}

@Article{LikKoc2007predictive,
  title =        {Predictive control of a gas--liquid separation plant based on
                  a {Gaussian} process model},
  author =       {Likar, Bojan and Kocijan, Ju{\v{s}}},
  journal =      compchemeng,
  volume =       31,
  number =       3,
  pages =        {142--152},
  year =         2007,
  publisher =    {Elsevier},
  doi = {10.1016/j.compchemeng.2006.05.011}
}

@Article{LinEggFeu2022smac3,
  author       = Lindauer_M #and# Eggensperger_K #and# Feurer_M #and#
                  Biedenkapp #and# {Difan Deng and Carolin Benjamins and Tim
                  Ruhkopf and René Sass} #and# Hutter,
  title        = {{SMAC3}: A Versatile Bayesian Optimization Package for
                  Hyperparameter Optimization},
  journal      = jmlr,
  year         = 2022,
  volume       = 23,
  pages        = {1--9},
  epub         = {http://jmlr.org/papers/v23/21-0888.html}
}

@Article{LinHooHutSch2015autofolio,
  title =        {{AutoFolio}: An Automatically Configured Algorithm Selector},
  author =       Lindauer_M #and# Hoos #and# Hutter #and# {Schaub, Torsten},
  journal =      jair,
  volume =       53,
  pages =        {745--778},
  year =         2015
}

@Article{LinKer73,
  author =       {S. Lin and B. W. Kernighan},
  title =        {An Effective Heuristic Algorithm for the Traveling
                  Salesman Problem},
  journal =      or,
  year =         1973,
  volume =       21,
  number =       2,
  pages =        {498--516},
}

@Article{LinVanKot2019,
  title =        {The algorithm selection competitions 2015 and 2017},
  author =       Lindauer_M #and# VanRijn_J #and# Kotthoff_L,
  journal =      aij,
  volume =       272,
  pages =        {86--100},
  year =         2019
}

@Article{LisWit2015tcs,
  title =        "Runtime Analysis of Ant Colony Optimization on Dynamic
                  Shortest Path Problems",
  journal =      tcs,
  volume =       561,
  number =       "Part A",
  pages =        "73--85",
  year =         2015,
  doi =          "10.1016/j.tcs.2014.06.035",
  author =       Lissovoi #and# Witt,
  abstract =     "A simple ACO algorithm called $\lambda$-MMAS for dynamic
                  variants of the single-destination shortest paths problem is
                  studied by rigorous runtime analyses. Building upon previous
                  results for the special case of 1-MMAS, it is studied to what
                  extent an enlarged colony using $\lambda$ ants per vertex
                  helps in tracking an oscillating optimum. It is shown that
                  easy cases of oscillations can be tracked by a constant
                  number of ants. However, the paper also identifies more
                  involved oscillations that with overwhelming probability
                  cannot be tracked with any polynomial-size colony. Finally,
                  parameters of dynamic shortest-path problems which make the
                  optimum difficult to track are discussed. Experiments
                  illustrate theoretical findings and conjectures. "
}

@Article{LitMurSweKar63,
  author =       {J. D. C. Little and K. G. Murty and D. W. Sweeney and C. Karel},
  title =        {An Algorithm for the Traveling Salesman Problem},
  journal =      or,
  year =         1963,
  volume =       11,
  pages =        {972--989}
}

@Article{LiuMalWanBre2017vis,
  author =       " Shusen Liu "#and#" Dan Maljovec "#and#" Bei Wang "#and#"
                  Peer-Timo Bremer "#and#" Valerio Pascucci",
  title =        "Visualizing High-Dimensional Data: Advances in the Past
                  Decade",
  doi =          "10.1109/TVCG.2016.2640960",
  year =         2017,
  journal =      tvcg,
  volume =       23,
  number =       3,
}

@Article{LiuRee2001,
  author =       {Jiyin Liu} #and# Reeves_CR,
  title =        {Constructive and Composite Heuristic Solutions to the
                  {P//$\Sigma$Ci} Scheduling Problem},
  journal =      ejor,
  volume =       132,
  number =       2,
  pages =        {439--452},
  year =         2001,
  doi =          "10.1016/S0377-2217(00)00137-5",
}

@Article{LiuYenGon2018twoarch,
  title =        {A multimodal multiobjective evolutionary algorithm using
                  two-archive and recombination strategies},
  author =       {Liu, Yiping} #and# {Yen, Gary G.} #and# {Gong, Dunwei},
  journal =      tec,
  year =         2018,
  number =       4,
  pages =        {660--674},
  volume =       23,
}

@Article{LocSch1999mlsl,
  author       = {Locatelli, Marco and Schoen, Fabio},
  title        = {Random Linkage: a family of acceptance/rejection algorithms
                  for global optimisation.},
  journal      = mp,
  year         = 1999,
  volume       = 85,
  number       = 2,
  keywords     = {Multi-Level Single-Linkage (MLSL)}
}

@Article{LodMarMon2002,
  title =        {Two-dimensional packing problems: A survey},
  author =       Lodi #and# Martello_S #and# Monaci_M,
  journal =      ejor,
  volume =       141,
  number =       2,
  pages =        {241--252},
  year =         2002,
  doi =          {10.1016/S0377-2217(02)00123-6},
}

@Article{LodMarVig1999binpack,
  title =        {Heuristic and metaheuristic approaches for a class of
                  two-dimensional bin packing problems},
  author =       Lodi #and# Martello_S #and# Vigo_D,
  journal =      informsjoc,
  volume =       11,
  number =       4,
  pages =        {345--357},
  year =         1999,
  publisher =    informs,
  doi = 	 {10.1287/ijoc.11.4.345}
}

@Article{LodMarVig2004tspack,
  title =        {{TSpack}: a unified tabu search code for multi-dimensional bin
                  packing problems},
  author =       Lodi #and# Martello_S #and# Vigo_D,
  journal =      aor,
  volume =       131,
  number =       {1-4},
  pages =        {203--213},
  year =         2004,
  publisher =    {Springer},
  doi = 	 {10.1023/B:ANOR.0000039519.03572.08}
}

@Article{LodZar2017learning,
  title =        {On Learning and Branching: A Survey},
  author =       Lodi #and# {Zarpellon, Giulia},
  journal =      {TOP},
  volume =       25,
  pages =        {207--236},
  year =         2017,
  publisher =    {Springer}
}

@Article{LohHorLin2008antennas,
  author =       {Lohn, Jason D. and Hornby, Gregory S. and Linden, Derek S.},
  title =        {Human-competitive Evolved Antennas},
  journal =      ai-edam,
  volume =       22,
  number =       3,
  year =         2008,
  pages =        {235--247},
  doi =          {10.1017/s0890060408000164},
  publisher =    cup-pub,
  annote =       "Evolutionary optimization of antennas for NASA"
}

@Article{LopBlu2010cor,
  author =       Lopez-Ibanez #and# Blum,
  title =        "Beam-{ACO} for the travelling salesman problem with
                  time windows",
  journal =      cor,
  year =         2010,
  doi =          {10.1016/j.cor.2009.11.015},
  volume =       37,
  number =       9,
  pages =        "1570--1583",
  keywords =     "Ant colony optimization, Travelling salesman problem with
                  time windows, Hybridization",
  alias =        "LopBlu09tsptw",
  abstract =     {The travelling salesman problem with time windows is
                  a difficult optimization problem that arises, for
                  example, in logistics. This paper deals with the
                  minimization of the travel-cost. For solving this
                  problem, this paper proposes a Beam-ACO algorithm,
                  which is a hybrid method combining ant colony
                  optimization with beam search.  In general, Beam-ACO
                  algorithms heavily rely on accurate and
                  computationally inexpensive bounding information for
                  differentiating between partial solutions. This work
                  uses stochastic sampling as a useful alternative. An
                  extensive experimental evaluation on seven benchmark
                  sets from the literature shows that the proposed
                  Beam-ACO algorithm is currently a state-of-the-art
                  technique for the travelling salesman problem with
                  time windows when travel-cost optimization is
                  concerned.},
}

@Article{LopBlu2013asoc,
  author =       Lopez-Ibanez #and# Blum #and# Ohlmann #and# Thomas_BW,
  title =        {The Travelling Salesman Problem with Time Windows:
                  Adapting Algorithms from Travel-time to Makespan
                  Optimization},
  journal =      asoco,
  year =         2013,
  volume =       13,
  number =       9,
  pages =        {3806--3815},
  doi =          {10.1016/j.asoc.2013.05.009},
  epub =
                  {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2013-011.pdf},
}

@Article{LopBraPaq2021arxiv,
  author =       Lopez-Ibanez #and# Branke #and# Paquete,
  title =        "Reproducibility in Evolutionary Computation",
  journal =      arxiv # {20102.03380 [cs.AI]},
  year =         2021,
  url =          {https://arxiv.org/abs/2102.03380},
  abstract =     "Experimental studies are prevalent in Evolutionary
                  Computation (EC), and concerns about the reproducibility and
                  replicability of such studies have increased in recent times,
                  reflecting similar concerns in other scientific fields. In
                  this article, we suggest a classification of different types
                  of reproducibility that refines the badge system of the
                  Association of Computing Machinery (ACM) adopted by TELO. We
                  discuss, within the context of EC, the different types of
                  reproducibility as well as the concepts of artifact and
                  measurement, which are crucial for claiming
                  reproducibility. We identify cultural and technical obstacles
                  to reproducibility in the EC field. Finally, we provide
                  guidelines and suggest tools that may help to overcome some
                  of these reproducibility obstacles.",
  keywords =     "Evolutionary Computation, Reproducibility, Empirical study,
                  Benchmarking",
}

@Article{LopBraPaq2021telo,
  author =       Lopez-Ibanez #and# Branke #and# Paquete,
  title =        "Reproducibility in Evolutionary Computation",
  journal =      telo,
  year =         2021,
  volume =       1,
  number =       4,
  pages =        "1--21",
  doi =          "10.1145/3466624",
  epub =       "https://arxiv.org/abs/2102.03380",
  abstract =     "Experimental studies are prevalent in Evolutionary
                  Computation (EC), and concerns about the reproducibility and
                  replicability of such studies have increased in recent times,
                  reflecting similar concerns in other scientific fields. In
                  this article, we suggest a classification of different types
                  of reproducibility that refines the badge system of the
                  Association of Computing Machinery (ACM) adopted by TELO. We
                  discuss, within the context of EC, the different types of
                  reproducibility as well as the concepts of artifact and
                  measurement, which are crucial for claiming
                  reproducibility. We identify cultural and technical obstacles
                  to reproducibility in the EC field. Finally, we provide
                  guidelines and suggest tools that may help to overcome some
                  of these reproducibility obstacles.",
  keywords =     "Evolutionary Computation, Reproducibility, Empirical study,
                  Benchmarking",
}

@Article{LopDubPerStuBir2016irace,
  author =       Lopez-Ibanez #and# Dubois-Lacoste #and# Perez_L #and# Stuetzle
                  #and# Birattari,
  title =        "The {\rpackage{irace}} Package: Iterated Racing for Automatic
                  Algorithm Configuration",
  journal =      orp,
  year =         2016,
  supplement =   {http://iridia.ulb.ac.be/supp/IridiaSupp2016-003/},
  doi =          {10.1016/j.orp.2016.09.002},
  volume =       3,
  pages =        {43--58}
}

@Article{LopKesStu2017:cim,
  author = 	 Lopez-Ibanez #and# MarmionKs #and# Stuetzle,
  title = 	 {Automatic Design of Hybrid Metaheuristics from Algorithmic Components},
  journal = 	 {Submitted},
  year = 	 2017,
  optvolume = 	 {},
  optnumber = 	 {},
  optpages = 	 {},
}

@Article{LopPaqStu05:jmma,
  author =       Lopez-Ibanez #and# Paquete #and# Stuetzle,
  title =        "Hybrid Population-based Algorithms for the
                  Bi-objective Quadratic Assignment Problem",
  journal =      jmma,
  year =         2006,
  volume =       5,
  number =       1,
  pages =        {111--137},
  pdf =          {LopPaqStu04-techrepAIDA-04-11.pdf},
  doi =          {10.1007/s10852-005-9034-x},
  alias =        {LopPaqStu06:jmma},
  abstract =     {We present variants of an ant colony optimization
                  (MO-ACO) algorithm and of an evolutionary algorithm
                  (SPEA2) for tackling multi-objective combinatorial
                  optimization problems, hybridized with an iterative
                  improvement algorithm and the robust tabu search
                  algorithm. The performance of the resulting hybrid
                  stochastic local search (SLS) algorithms is
                  experimentally investigated for the bi-objective
                  quadratic assignment problem (bQAP) and compared
                  against repeated applications of the underlying
                  local search algorithms for several
                  scalarizations. The experiments consider structured
                  and unstructured bQAP instances with various degrees
                  of correlation between the flow matrices. We do a
                  systematic experimental analysis of the algorithms
                  using outperformance relations and the attainment
                  functions methodology to asses differences in the
                  performance of the algorithms. The experimental
                  results show the usefulness of the hybrid algorithms
                  if the available computation time is not too limited
                  and identify SPEA2 hybridized with very short tabu
                  search runs as the most promising variant.}
}

@Article{LopPerStu2020ifors,
  author =       Lopez-Ibanez #and# Perez_L #and# Stuetzle,
  title =        {{irace}: A Tool for the Automatic Configuration of
                  Algorithms},
  journal =      {International Federation of Operational Research Societies
                  (IFORS) News},
  year =         2020,
  volume =       14,
  number =       2,
  pages =        {30--32},
  month =        jun,
  url =          {https://www.ifors.org/newsletter/ifors-news-june2020.pdf}
}

@Article{LopPraPae08aco,
  author =       Lopez-Ibanez #and# Prasad #and# Paechter,
  title =        {Ant Colony Optimisation for the Optimal Control of
                  Pumps in Water Distribution Networks},
  journal =      jwrpm,
  year =         2008,
  volume =       134,
  number =       4,
  pages =        {337--346},
  publisher =    "{ASCE}",
  pdf =          {LopezPrasadPaechter08-jwrpm.pdf},
  epub =          {http://link.aip.org/link/?QWR/134/337/1},
  doi =         {10.1061/(ASCE)0733-9496(2008)134:4(337)},
abstract = {Reducing energy consumption of water distribution
  networks has never had more significance than today. The greatest
  energy savings can be obtained by careful scheduling of operation of
  pumps. Schedules can be defined either implicitly, in terms of other
  elements of the network such as tank levels, or explicitly by
  specifying the time during which each pump is on/off. The
  traditional representation of explicit schedules is a string of
  binary values with each bit representing pump on/off status during a
  particular time interval. In this paper a new explicit
  representation is presented. It is based on time controlled
  triggers, where the maximum number of pump switches is specified
  beforehand. In this representation a pump schedule is divided into a
  series of integers with each integer representing the number of
  hours for which a pump is active/inactive. This reduces the number
  of potential schedules (search space) compared to the binary
  representation. Ant colony optimization (ACO) is a stochastic
  meta-heuristic for combinatorial optimization problems that is
  inspired by the foraging behavior of some species of ants. In this
  paper, an application of the ACO framework was developed for the
  optimal scheduling of pumps. The proposed representation was adapted
  to an ant colony Optimization framework and solved for the optimal
  pump schedules. Minimization of electrical cost was considered as
  the objective, while satisfying system constraints. Instead of using
  a penalty function approach for constraint violations, constraint
  violations were ordered according to their importance and solutions
  were ranked based on this order. The proposed approach was tested on
  a small test network and on a large real-world network. Results are
  compared with those obtained using a simple genetic algorithm based
  on binary representation and a hybrid genetic algorithm that uses
  level-based triggers.}
}

@Article{LopPraPae2011ec,
  author =       Lopez-Ibanez #and# Prasad #and# Paechter,
  title =        "Representations and Evolutionary Operators for the
                  Scheduling of Pump Operations in Water Distribution
                  Networks",
  journal =      ec,
  year =         2011,
  doi =          {10.1162/EVCO_a_00035},
  volume =       19,
  number =       3,
  pages =        {429--467},
  abstract =     {Reducing the energy consumption of water
                  distribution networks has never had more
                  significance. The greatest energy savings can be
                  obtained by carefully scheduling the operations of
                  pumps. Schedules can be defined either implicitly,
                  in terms of other elements of the network such as
                  tank levels, or explicitly by specifying the time
                  during which each pump is on/off.  The traditional
                  representation of explicit schedules is a string of
                  binary values with each bit representing pump on/off
                  status during a particular time interval.  In this
                  paper, we formally define and analyze two new
                  explicit representations based on time-controlled
                  triggers, where the maximum number of pump switches
                  is established beforehand and the schedule may
                  contain less switches than the maximum. In these
                  representations, a pump schedule is divided into a
                  series of integers with each integer representing
                  the number of hours for which a pump is
                  active/inactive.  This reduces the number of
                  potential schedules compared to the binary
                  representation, and allows the algorithm to operate
                  on the feasible region of the search space.  We
                  propose evolutionary operators for these two new
                  representations. The new representations and their
                  corresponding operations are compared with the two
                  most-used representations in pump scheduling,
                  namely, binary representation and level-controlled
                  triggers. A detailed statistical analysis of the
                  results indicates which parameters have the greatest
                  effect on the performance of evolutionary
                  algorithms. The empirical results show that an
                  evolutionary algorithm using the proposed
                  representations improves over the results obtained
                  by a recent state-of-the-art Hybrid Genetic
                  Algorithm for pump scheduling using level-controlled
                  triggers.}
}

@Article{LopStu2012swarm,
  author  = Lopez-Ibanez #and# Stuetzle,
  title   = "An experimental analysis of design choices of multi-objective ant colony optimization algorithms",
  journal = swarm,
  year    = 2012,
  number = 3,
  volume = 6,
  pages = {207--232},
  doi     = {10.1007/s11721-012-0070-7},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-006/},
}

@Article{LopStu2012tec,
  author =       Lopez-Ibanez #and# Stuetzle,
  title =        "The Automatic Design of Multi-Objective Ant Colony
                  Optimization Algorithms",
  journal =      tec,
  year =         2012,
  volume =       16,
  number =       6,
  pages =        {861--875},
  doi = {10.1109/TEVC.2011.2182651},
  abstract = {
Multi-objective optimization problems are problems with several,
typically conflicting criteria for evaluating solutions. Without
any a priori preference information, the Pareto optimality
principle establishes a partial order among solutions, and the
output of the algorithm becomes a set of nondominated solutions
rather than a single one. Various ant colony optimization (ACO)
algorithms have been proposed in recent years for solving such
problems. These multi-objective ACO (MOACO) algorithms exhibit
different design choices for dealing with the particularities of
the multi-objective context. This paper proposes a formulation of
algorithmic components that suffices to describe most MOACO
algorithms proposed so far. This formulation also shows that
existing MOACO algorithms often share equivalent design choices
but they are described in different terms. Moreover, this
formulation is synthesized into a flexible algorithmic framework,
from which not only existing MOACO algorithms may be
instantiated, but also combinations of components that were never
studied in the literature. In this sense, this paper goes beyond
proposing a new MOACO algorithm, but it rather introduces a
family of MOACO algorithms. The flexibility of the proposed MOACO
framework facilitates the application of automatic algorithm
configuration techniques. The experimental results presented in
this paper show that the automatically configured MOACO framework
outperforms the MOACO algorithms that inspired the framework
itself. This paper is also among the first to apply automatic
algorithm configuration techniques to multi-objective algorithms.}
}

@Article{LopStu2013ejor,
  author =       Lopez-Ibanez #and# Stuetzle,
  title =        "Automatically Improving the Anytime Behaviour of Optimisation
                  Algorithms",
  journal =      ejor,
  year =         2014,
  volume =       235,
  number =       3,
  pages =        {569--582},
  doi =          "10.1016/j.ejor.2013.10.043",
  pdf =          "LopStu2014ejor.pdf",
  supplement =   {http://iridia.ulb.ac.be/supp/IridiaSupp2012-011/},
  abstract =     {Optimisation algorithms with good anytime behaviour try to
                  return as high-quality solutions as possible independently of
                  the computation time allowed. Designing algorithms with good
                  anytime behaviour is a difficult task, because performance is
                  often evaluated subjectively, by plotting the trade-off curve
                  between computation time and solution quality. Yet, the
                  trade-off curve may be modelled also as a set of mutually
                  nondominated, bi-objective points. Using this model, we
                  propose to combine an automatic configuration tool and the
                  hypervolume measure, which assigns a single quality measure
                  to a nondominated set. This allows us to improve the anytime
                  behaviour of optimisation algorithms by means of
                  automatically finding algorithmic configurations that produce
                  the best nondominated sets. Moreover, the recently proposed
                  weighted hypervolume measure is used here to incorporate the
                  decision-maker's preferences into the automatic tuning
                  procedure. We report on the improvements reached when
                  applying the proposed method to two relevant scenarios: (i)
                  the design of parameter variation strategies for MAX-MIN Ant
                  System and (ii) the tuning of the anytime behaviour of SCIP,
                  an open-source mixed integer programming solver with more
                  than 200 parameters.}
}

@Article{LopTerRos2014esa,
  author =       {Eunice López-Camacho and Hugo Terashima-Marin} #and# Ross_P #and# Ochoa,
  title =        {A unified hyper-heuristic framework for solving bin packing
                  problems},
  journal =      eswa,
  volume =       41,
  number =       15,
  pages =        {6876--6889},
  year =         2014,
  doi =          "10.1016/j.eswa.2014.04.043",
}

@Article{LouBoi2008vns_anytime,
  author =       Loudni_S #and# Boizumault,
  title =        "Combining {VNS} with constraint programming for
                  solving anytime optimization problems",
  journal =      ejor,
  year =         2008,
  volume =       191,
  pages =        {705--735},
  doi =          "10.1016/j.ejor.2006.12.062",
}

@Article{Lourenco1995,
  author =       Lourenco_HR,
  title =        {Job-Shop Scheduling: Computational Study of Local
  Search and Large-Step Optimization Methods},
  journal =      ejor,
  year =         1995,
  volume =       83,
  number =       2,
  pages =        {347--364}
}

@Article{LovTor2001aor,
  author =       {Lova, Antonio and Tormos, Pilar},
  title =        {Analysis of Scheduling Schemes and Heuristic Rules
                  Performance in Resource-Constrained Multiproject
                  Scheduling},
  volume =       102,
  doi =          {10.1023/A:1010966401888},
  abstract =     {Frequently, the availability of resources assigned
                  to a project is limited and not sufficient to
                  execute all the concurrent activities. In this
                  situation, decision making about their schedule is
                  necessary. Many times this schedule supposes an
                  increase in the project completion
                  time. Additionally, companies commonly manage
                  various projects simultaneously, sharing a pool of
                  renewable resources. Given these resource
                  constraints, we often can only apply heuristic
                  methods to solve the scheduling problem. In this
                  work the effect of the schedule generation schemes -
                  serial or parallel - and priority rules - {MINLFT},
                  {MINSLK}, {MAXTWK}, {SASP} or {FCFS} - with two
                  approaches - multi-project and single-project - are
                  analysed. The time criteria considered are the mean
                  project delay and the multiproject duration
                  increase. Through an extensive computational study,
                  results show that with the parallel scheduling
                  generation scheme and the multi-project approach the
                  project manager can obtain a good multiproject
                  schedule with the time criterion selected:
                  minimising mean project delay or minimising
                  multiproject duration increase. New heuristics -
                  based on priority rules with a two-phase approach -
                  that outperform classical ones are proposed to
                  minimise mean project delay with a multi-project
                  approach. Finally, the best heuristics analysed are
                  evaluated together with a representative sample of
                  commercial project management software.},
  number =       {1-4},
  journal =      aor,
  month =        feb,
  year =         2001,
  keywords =     {Combinatorics, heuristic based on priority rules,
                  Multiproject scheduling, Operation
                  {Research/Decision} Theory, Project management,
                  project management software, Resource allocation,
                  Theory of Computation},
  pages =        {263--286},
}

@Article{LovTorCer2009ijpe,
  author =       {Lova, Antonio and Tormos, Pilar and Cervantes,
                  Mariamar and Barber, Federico},
  title =        {An efficient hybrid genetic algorithm for scheduling
                  projects with resource constraints and multiple
                  execution modes},
  volume =       117,
  doi =          {10.1016/j.ijpe.2008.11.002},
  abstract =     {Multi-mode Resource Constrained Project Scheduling
                  Problem ({MRCPSP)} aims at finding the start times
                  and execution modes for the activities of a project
                  that optimize a given objective function while
                  verifying a set of precedence and resource
                  constraints. In this paper, we focus on this problem
                  and develop a hybrid Genetic Algorithm ({MM-HGA)} to
                  solve it. Its main contributions are the mode
                  assignment procedure, the fitness function and the
                  use of a very efficient improving method. Its
                  performance is demonstrated by extensive
                  computational results obtained on a set of standard
                  instances and against the best currently available
                  algorithms.},
  number =       2,
  journal =      ijpe,
  year =         2009,
  keywords =     {genetic algorithm, multi-mode resource-constrained
                  project scheduling},
  pages =        {302--316},
}

@Article{LozGloGarRodMar2014,
  author =       Lozano_M #and# Glover_F #and# Garcia-Martinez #and# Rodriguez_F #and# Marti_R,
  title =        {Tabu Search with Strategic Oscillation for the Quadratic Minimum Spanning Tree},
  journal =      iiet,
  year =         2014,
  volume =       46,
  number =       4,
  pages =        {414--428}
}

@Article{LozMolGar2011,
  author =       Lozano_M #and# Molina_D #and# Garcia-Martinez,
  title =        {Iterated Greedy for the Maximum Diversity Problem},
  journal =      ejor,
  year =         2011,
  volume =       214,
  number =       1,
  pages =        {31--38}
}

@Article{LuGloHao2010ejor,
  author =       Lu_Zhipeng #and# Glover_F #and# Hao_JK,
  title =        {A hybrid metaheuristic approach to solving the
                  {UBQP} problem},
  journal =      ejor,
  volume =       207,
  number =       3,
  pages =        {1254--1262},
  year =         2010,
  doi =          {10.1016/j.ejor.2010.06.039},
}

@Article{Lucas2014ising,
  title =        {Ising formulations of many {NP} problems},
  author =       {Lucas, Andrew},
  journal =      frontphys,
  volume =       2,
  pages =        5,
  year =         2014,
  publisher =    {Frontiers},
  doi =          "10.3389/fphy.2014.00005"
}

@Article{LundyMees1986,
  title =        {Convergence of an Annealing Algorithm},
  author =       Lundy_M #and# Mees_A,
  journal =      mp,
  volume =       34,
  number =       1,
  pages =        {111--124},
  year =         1986,
}

@Article{LusTeg2009tpls,
  author =       Lust #and# Teghem,
  title =        "Two-phase {Pareto} local search for the biobjective traveling
                  salesman problem",
  doi =          {10.1007/s10732-009-9103-9},
  abstract =     {In this work, we present a method, called {Two-Phase}
                  {Pareto} Local Search, to find a good approximation of the
                  efficient set of the biobjective traveling salesman
                  problem. In the first phase of the method, an initial
                  population composed of a good approximation of the extreme
                  supported efficient solutions is generated. We use as second
                  phase a {Pareto} Local Search method applied to each solution
                  of the initial population. We show that using the combination
                  of these two techniques: good initial population generation
                  plus {Pareto} Local Search gives better results than
                  state-of-the-art algorithms. Two other points are introduced:
                  the notion of ideal set and a simple way to produce
                  near-efficient solutions of multiobjective problems, by using
                  an efficient single-objective solver with a data perturbation
                  technique.  },
  journal =      joh,
  volume =       16,
  number =       3,
  pages =        {475--510},
  year =         2010,
  alias =        "Lust09",
}

@Article{LusTeg2010arxiv,
  author =       Lust #and# Teghem,
  title =        "The multiobjective multidimensional knapsack
                  problem: a survey and a new approach",
  journal =      arxiv # {1007.4063},
  year =         2010,
  note = {Published as~\cite{LusTeg2012itor}}
}

@Article{LusTeg2012itor,
  title =        "The multiobjective multidimensional knapsack
                  problem: a survey and a new approach",
  author =       Lust #and# Teghem,
  journal =      itor,
  volume =       19,
  number =       4,
  pages =        {495--520},
  year =         2012,
  doi =          {10.1111/j.1475-3995.2011.00840.x},
}

@Article{LustJasz09btsp,
  author =       Lust #and# Jaszkiewicz,
  title =        "Speed-up techniques for solving large-scale biobjective
                  {TSP}",
  journal =      cor,
  year =         2010,
  doi =          "10.1016/j.cor.2009.01.005",
  pages =        "521--533",
  volume =       37,
  number =       3,
  keywords =     "Multiobjective combinatorial optimization, Hybrid
                  metaheuristics, TSP, Local search, Speed-up techniques"
}

@Article{LuvBarBri2014,
  title =        {A survey on multi-objective evolutionary algorithms
                  for many-objective problems},
  author =       vonLucken #and# Baran #and# { Brizuela, Carlos},
  pages =        {707--756},
  year =         2014,
  journal =      coa,
  volume =       58,
  number =       3,
}

@Article{MaaHin2008tsne,
  author =       {Laurens van der Maaten and Geoffrey Hinton},
  title =        {Visualizing Data using t-{SNE}},
  journal =      jmlr,
  year =         2008,
  volume =       9,
  number =       86,
  pages =        {2579--2605},
  epub = "http://jmlr.org/papers/v9/vandermaaten08a.html"
}

@Article{MachBelTal2018ale,
  author =       {Machado, Marlos C. and Bellemare, Marc G. and Talvitie, Erik
                  and Veness, Joel and Hausknecht, Matthew and Bowling,
                  Michael},
  title =        {Revisiting the {Arcade} {Learning} {Environment}: Evaluation
                  Protocols and Open Problems for General Agents},
  year =         2018,
  publisher =    {AI Access Foundation},
  address =      {El Segundo, CA, USA},
  volume =       61,
  number =       1,
  issn =         {1076-9757},
  abstract =     {The Arcade Learning Environment (ALE) is an evaluation
                  platform that poses the challenge of building AI agents with
                  general competency across dozens of Atari 2600 games. It
                  supports a variety of different problem settings and it has
                  been receiving increasing attention from the scientific
                  community, leading to some high-pro_le success stories such
                  as the much publicized Deep Q-Networks (DQN). In this article
                  we take a big picture look at how the ALE is being used by
                  the research community. We show how diverse the evaluation
                  methodologies in the ALE have become with time, and highlight
                  some key concerns when evaluating agents in the ALE. We use
                  this discussion to present some methodological best practices
                  and provide new benchmark results using these best
                  practices. To further the progress in the field, we introduce
                  a new version of the ALE that supports multiple game modes
                  and provides a form of stochasticity we call sticky
                  actions. We conclude this big picture look by revisiting
                  challenges posed when the ALE was introduced, summarizing the
                  state-of-the-art in various problems and highlighting
                  problems that remain open.},
  journal =      jair,
  month =        jan,
  pages =        {523--562},
  numpages =     40
}

@Article{Madden2012,
  title =        {From Databases to Big Data},
  author =       {Madden, Sam},
  journal =      {IEEE Internet Computing},
  volume =       16,
  number =       3,
  year =         2012
}

@Article{MahFesDam2007harmony,
  author =       {M. Mahdavi and M. Fesanghary and E. Damangir},
  title =        "An improved harmony search algorithm for solving optimization
                  problems",
  journal =      amc,
  volume =       188,
  number =       2,
  pages =        "1567--1579",
  year =         2007,
  doi =          "10.1016/j.amc.2006.11.033",
  keywords =     "Global optimization, Heuristics, Harmony search algorithm,
                  Mathematical programming",
  abstract =     "This paper develops an Improved harmony search (IHS)
                  algorithm for solving optimization problems. IHS employs a
                  novel method for generating new solution vectors that
                  enhances accuracy and convergence rate of harmony search (HS)
                  algorithm. In this paper the impacts of constant parameters
                  on harmony search algorithm are discussed and a strategy for
                  tuning these parameters is presented. The IHS algorithm has
                  been successfully applied to various benchmarking and
                  standard engineering optimization problems. Numerical results
                  reveal that the proposed algorithm can find better solutions
                  when compared to HS and other heuristic or deterministic
                  methods and is a powerful search algorithm for various
                  engineering optimization problems."
}

@Article{MaiRon2012,
  title =        {New heuristics for total tardiness minimization in
                  a flexible flowshop},
  author =       {Mainieri, Guilherme B. and Ronconi, D{\'e}bora P.},
  journal =      ol,
  pages =        {1--20},
  year =         2012
}

@Article{MaieSimp03:ACODesignWDN,
  author =       Maier #and# Simpson #and# Zecchin #and# Foong_WK #and#
                  Phang_KY #and# Seah_HY #and# Tan_CL,
  title =        {Ant Colony Optimization for Design of Water Distribution
                  Systems},
  journal =      jwrpm,
  volume =       129,
  number =       3,
  pages =        {200--209},
  date =         {2003-05/2003-06},
  year =         2003,
  month =        may #" / "#jun,
}

@Article{MMalDas2022twiarch,
  title =        {A twin-archive guided decomposition based
                  multi/many-objective evolutionary algorithm},
  author = {M, Sri Srinivasa Raju} #and# {Mallipeddi, Rammohan} #and# {Das, Kedar Nath},
  journal =      swevo,
  volume =       71,
  pages =        101082,
  year =         2022,
  doi =          "10.1016/j.swevo.2022.101082",
  publisher =    {Elsevier}
}

@Article{MalEng2013survey,
  author =       {Katherine M. Malan}  #and# Engelbrecht_A,
  title =        {A survey of techniques for characterising fitness landscapes
                  and some possible ways forward},
  journal =      is,
  volume =       241,
  pages =        {148--163},
  year =         2013,
  issn =         {0020-0255},
  doi =          {10.1016/j.ins.2013.04.015},
}

@Article{Males85,
  author =       Males #and# Clark #and# Wehrman #and# Gateset,
  title =        "Algorithm for mixing problems in water systems",
  journal =      jhe,
  year =         1985,
  volume =       111,
  number =       2,
  pages =        {206--219},
}

@Article{Man1999:joc,
  author =       Maniezzo,
  title =        {Exact and Approximate Nondeterministic Tree-Search Procedures
                  for the Quadratic Assignment Problem},
  journal =      informsjoc,
  year =         1999,
  volume =       11,
  number =       4,
  pages =        {358--369},
  alias =        {Man99:informs}
}

@Article{ManCar2000:fgcs,
  author =       Maniezzo #and# Carbonaro,
  title =        "An {ANTS} Heuristic for the Frequency Assignment Problem",
  journal =      fgcs,
  year =         2000,
  volume =       16,
  number =       8,
  pages =        "927--935",
  alias =        {ManCar00}
}

@Article{ManCol99,
  author =       Maniezzo #and# Colorni,
  title =        "The {Ant} {System} Applied to the Quadratic
                  Assignment Problem",
  journal =      tkde,
  year =         1999,
  volume =       11,
  number =       5,
  pages =        {769--778}
}

@Article{Mar84,
  title =        "On a multicritera shortest path problem",
  journal =      ejor,
  volume =       16,
  pages =        "236--245",
  year =         1984,
  author =       "E. Q. V. Martins"
}

@Article{MarAro2004smo,
  author =       {Marler, R. T. and Arora, J. S.},
  title =        {Survey of multi-objective optimization methods for
                  engineering},
  journal =      smo,
  year =         2004,
  volume =       26,
  number =       6,
  pages =        {369--395},
  month =        apr,
  doi =          {10.1007/s00158-003-0368-6},
  annote = "Discusses a priori (scalarized) methods."
}

@Article{MarCavHer2023repr,
  author       = Martin-Santamaria_R #and# {Cavero, Sergio and Herrán, Alberto}
                  #and# Duarte_A #and# Colmenar_JM,
  title        = {A Practical Methodology for Reproducible Experimentation: An
                  Application to the Double-Row Facility Layout Problem},
  journal      = ec,
  year         = 2023,
  pages        = {1--35},
  month        = nov,
  issn         = {1063-6560},
  doi          = {10.1162/evco_a_00317},
  publisher    = {MIT Press},
  keywords     = {irace}
}

@Article{MarDeBHaeVanSnoBae07,
  author =       {D. Martens and M. De Backer and R. Haesen and
                  J. Vanthienen and M. Snoeck and B. Baesens},
  title =        {Classification With Ant Colony Optimization},
  journal =      tec,
  year =         2007,
  volume =       11,
  number =       5,
  pages =        {651--665},
}

@Article{MarLopStuCol2024auto,
  author       = Martin-Santamaria_R #and# Lopez-Ibanez #and# Stuetzle #and#
                  Colmenar_JM,
  title        = {On the automatic generation of metaheuristic algorithms for
                  combinatorial optimization problems},
  journal      = ejor,
  year         = 2024,
  doi          = {10.1016/j.ejor.2024.06.001},
  abstract     = {Metaheuristic algorithms have become one of the preferred
                  approaches for solving optimization problems. Finding the
                  best metaheuristic for a given problem is often difficult due
                  to the large number of available approaches and possible
                  algorithmic designs. Moreover, high-performing metaheuristics
                  often combine general-purpose and problem-specific
                  algorithmic components. We propose here an approach for
                  automatically designing metaheuristics using a flexible
                  framework of algorithmic components, from which algorithms
                  are instantiated and evaluated by an automatic configuration
                  method. The rules for composing algorithmic components are
                  defined implicitly by the properties of each algorithmic
                  component, in contrast to previous proposals, which require a
                  handwritten algorithmic template or grammar. As a result,
                  extending our framework with additional components, even
                  problem-specific or user-defined ones, automatically updates
                  the design space. Furthermore, since the generated algorithms
                  are made up of components, they can be easily interpreted. We
                  provide an implementation of our proposal and demonstrate its
                  benefits by outperforming previous research in three distinct
                  problems from completely different families: a facility
                  layout problem, a vehicle routing problem and a clustering
                  problem.},
  keywords     = {irace}
}

@Article{MarMarWeiWol2002,
  title =        {Cutting planes in integer and mixed integer programming},
  author =       {Marchand, Hugues and Martin, Alexander and Weismantel, Robert and Wolsey, Laurence},
  journal =      dam,
  volume =       123,
  number =       {1--3},
  pages =        {397--446},
  year =         2002,
  publisher =    elsevier
}

@Article{MarMoo1997air,
  author       = {O. Maron and A. W. Moore},
  title        = {The Racing Algorithm: {Model} Selection for Lazy Learners},
  journal      = aires,
  year         = 1997,
  volume       = 11,
  number       = {1--5},
  pages        = {193--225},
  doi          = {10.1023/A:1006556606079}
}

@Article{MarOtt1995,
  author =       Martin_O #and# {S. W. Otto},
  title =        {Partitioning of Unstructured Meshes for Load
                  Balancing},
  journal =      cpe,
  year =         1995,
  volume =       7,
  number =       4,
  pages =        {303--314}
}

@Article{MarOtt1996:aor,
  author =       Martin_O #and# {S. W. Otto},
  title =        {Combining Simulated Annealing with Local Search
                  Heuristics},
  journal =      aor,
  year =         1996,
  volume =       63,
  pages =        {57--75}
}

@Article{MarOttFel91:cs,
  author =       Martin_O #and# {S. W. Otto and E. W. Felten},
  title =        {Large-Step {Markov} Chains for the Traveling
                  Salesman Problem},
  journal =      cs,
  year =         1991,
  volume =       5,
  number =       3,
  pages =        {299--326}
}

@Article{MarOttFel92:orl,
  author =       Martin_O #and# {S. W. Otto and E. W. Felten},
  title =        {Large-step {Markov} Chains for the {TSP}
                  Incorporating Local Search Heuristics},
  journal =      orl,
  year =         1992,
  volume =       11,
  number =       4,
  pages =        {219--224}
}

@Article{MarReiDua2012,
  author =       Marti_R #and# Reinelt #and# Duarte_A,
  title =        {A Benchmark Library and a Comparison of Heuristic Methods for the Linear Ordering Problem},
  journal =      coa,
  year =         2012,
  volume =       51,
  number =       3,
  pages =        {1297--1317}
}

@Article{MarSanPer2022strategic,
  author       = Martin-Santamaria_R #and# Sanchez-Oro_J #and#
                  {S. P\'{e}rez-Pel\'{o}} #and# Duarte_A,
  title        = {Strategic oscillation for the balanced minimum sum-of-squares
                  clustering problem},
  journal      = is,
  year         = 2022,
  volume       = 585,
  pages        = {529--542},
  doi          = {10.1016/j.ins.2021.11.048}
}

@Article{MarTot1990dam,
  author =       Martello_S #and# Toth_P,
  title =        {Lower bounds and reduction procedures for the bin
                  packing problem},
  journal =      dam,
  volume =       28,
  number =       1,
  year =         1990,
  pages =        {59--70},
  doi =          {10.1016/0166-218X(90)90094-S},
}

@Article{MarVig1998exact,
  title =        {Exact solution of the two-dimensional finite bin packing
                  problem},
  author =       Martello_S #and# Vigo_D,
  journal =      ms,
  volume =       44,
  number =       3,
  pages =        {388--399},
  year =         1998,
  publisher =    informs,
  doi =          {10.1287/mnsc.44.3.388}
}

@Article{MasHwa1981isgp,
  author       = {Masud, Abu S. and Hwang, C. L.},
  title        = {Interactive Sequential Goal Programming},
  journal      = jors,
  year         = 1981,
  volume       = 32,
  number       = 5,
  pages        = {391--400},
  month        = may,
  issn         = {1476-9360},
  doi          = {10.1057/jors.1981.76},
  abstract     = {This paper introduces a new solution method based on Goal
                  Programming for Multiple Objective Decision Making (MODM)
                  problems. The method, called Interactive Sequential Goal
                  Programming (ISGP), combines and extends the attractive
                  features of both Goal Programming and interactive solution
                  approaches for MODM problems. ISGP is applicable to both
                  linear and non-linear problems. It uses existing single
                  objective optimization techniques and, hence, available
                  computer codes utilizing these techniques can be adapted for
                  use in ISGP. The non-dominance of the "best-compromise"
                  solution is assured. The information required from the
                  decision maker in each iteration is simple. The proposed
                  method is illustrated by solving a nutrition problem.}
}

@Article{MasLopDubStu2014cor,
  author =       Mascia_F #and# Lopez-Ibanez #and# Dubois-Lacoste #and#
                  Stuetzle,
  title =        "Grammar-Based Generation of Stochastic Local Search
                  Heuristics through Automatic Algorithm Configuration Tools",
  journal =      cor,
  year =         2014,
  doi =          {10.1016/j.cor.2014.05.020},
  pdf =          {MasLopDubStu2014cor.pdf},
  volume =       51,
  pages =        {190--199},
}

@Article{MasPelStuBir2014itor,
  author =       Mascia_F #and# Pellegrini #and# Stuetzle #and# Birattari,
  title =        "An Analysis of Parameter Adaptation in Reactive Tabu Search",
  journal =      itor,
  year =         2014,
  volume =       21,
  number =       1,
  pages =        {127--152},
}

@Article{MasVidMic++2013,
  author =       {Renaud Masson} #and# Vidal_T #and# {Julien Michallet and Puca Huachi {Vaz Penna} and Vinicius Petrucci} #and# Subramanian #and# {Hugues Dubedout},
  title =        {An Iterated Local Search Heuristic for Multi-capacity Bin Packing and Machine Reassignment Problems},
  journal =      eswa,
  year =         2013,
  volume =       40,
  number =       13,
  pages =        {5266--5275}
}

@Article{MatDauLah2011:ejor,
  author =       {Yazid Mati} #and# Dauzere #and# {Chams Lahlou},
  title =        {A General Approach for Optimizing Regular Criteria in the Job-shop Scheduling Problem},
  journal =      ejor,
  year =         2011,
  volume =       212,
  number =       1,
  pages =        {33--42}
}

@Article{MazLopChuMie2023tgp,
  author       = Mazumdar_A #and# Lopez-Ibanez #and# Chugh_Tinkle #and#
                  Miettinen,
  title        = {Treed {Gaussian} Process Regression for Solving Offline
                  Data-Driven Continuous Multiobjective Optimization Problems},
  journal      = ec,
  year         = 2023,
  volume       = 31,
  number       = 4,
  pages        = {375--399},
  doi          = {10.1162/evco_a_00329},
  abstract     = {For offline data-driven multiobjective optimization problems
                  (MOPs), no new data is available during the optimization
                  process. Approximation models (or surrogates) are first built
                  using the provided offline data and an optimizer, e.g. a
                  multiobjective evolutionary algorithm, can then be utilized
                  to find Pareto optimal solutions to the problem with
                  surrogates as objective functions. In contrast to online
                  data-driven MOPs, these surrogates cannot be updated with new
                  data and, hence, the approximation accuracy cannot be
                  improved by considering new data during the optimization
                  process. Gaussian process regression (GPR) models are widely
                  used as surrogates because of their ability to provide
                  uncertainty information. However, building GPRs becomes
                  computationally expensive when the size of the dataset is
                  large. Using sparse GPRs reduces the computational cost of
                  building the surrogates. However, sparse GPRs are not
                  tailored to solve offline data-driven MOPs, where good
                  accuracy of the surrogates is needed near Pareto optimal
                  solutions. Treed GPR (TGPR-MO) surrogates for offline
                  data-driven MOPs with continuous decision variables are
                  proposed in this paper. The proposed surrogates first split
                  the decision space into subregions using regression trees and
                  build GPRs sequentially in regions close to Pareto optimal
                  solutions in the decision space to accurately approximate
                  tradeoffs between the objective functions. TGPR-MO surrogates
                  are computationally inexpensive because GPRs are built only
                  in a smaller region of the decision space utilizing a subset
                  of the data. The TGPR-MO surrogates were tested on
                  distance-based visualizable problems with various data sizes,
                  sampling strategies, numbers of objective functions, and
                  decision variables. Experimental results showed that the
                  TGPR-MO surrogates are computationally cheaper and can handle
                  datasets of large size. Furthermore, TGPR-MO surrogates
                  produced solutions closer to Pareto optimal solutions
                  compared to full GPRs and sparse GPRs.},
  keywords     = {Gaussian processes, Kriging, Regression trees, Metamodelling,
                  Surrogate, Pareto optimality}
}

@Article{McConMehNah2011certifying,
  author       = {Ross M. McConnell and Kurt Mehlhorn and Stefan N{\"a}her and
                  Pascal Schweitzer},
  title        = {Certifying algorithms},
  journal      = csrev,
  year         = 2011,
  volume       = 5,
  number       = 2,
  pages        = {119--161},
  issn         = {1574-0137},
  doi          = {10.1016/j.cosrev.2010.09.009},
  keywords     = {Algorithms, Software reliability, Certification},
  abstract     = {A certifying algorithm is an algorithm that produces, with
                  each output, a certificate or witness (easy-to-verify proof)
                  that the particular output has not been compromised by a
                  bug. A user of a certifying algorithm inputs x, receives the
                  output y and the certificate w, and then checks, either
                  manually or by use of a program, that w proves that y is a
                  correct output for input x. In this way, he/she can be sure
                  of the correctness of the output without having to trust the
                  algorithm. We put forward the thesis that certifying
                  algorithms are much superior to non-certifying algorithms,
                  and that for complex algorithmic tasks, only certifying
                  algorithms are satisfactory. Acceptance of this thesis would
                  lead to a change of how algorithms are taught and how
                  algorithms are researched. The widespread use of certifying
                  algorithms would greatly enhance the reliability of
                  algorithmic software. We survey the state of the art in
                  certifying algorithms and add to it. In particular, we start
                  a theory of certifying algorithms and prove that the concept
                  is universal.}
}

@Article{McCorPow03demand,
  title =        {Optimal Pump Scheduling in Water Supply Systems with
                  Maximum Demand Charges},
  author =       McCormick #and# Powell_RS,
  publisher =    {ASCE},
  year =         2003,
  journal =      jwrpm,
  volume =       129,
  number =       5,
  pages =        {372--379},
  keywords =     {water supply; pumps; Markov processes; cost optimal
                  control},
  epub =          {http://link.aip.org/link/?QWR/129/372/1},
  doi =          {10.1061/(ASCE)0733-9496(2003)129:5(372)}
}

@Article{McCormick04,
  author       = McCormick #and# Powell_RS,
  title        = {Derivation of near-optimal pump schedules for water
                  distribution by simulated annealing},
  journal      = jors,
  year         = 2004,
  volume       = 55,
  number       = 7,
  pages        = {728--736},
  month        = jul,
  doi          = {10.1057/palgrave.jors.2601718},
  abstract     = {The scheduling of pumps for clean water distribution is a
                  partially discrete non-linear problem with many
                  variables. The scheduling method described in this paper
                  typically produces costs within 1\% of a linear program-based
                  solution, and can incorporate realistic non-linear costs that
                  may be hard to incorporate in linear programming
                  formulations. These costs include pump switching and maximum
                  demand charges. A simplified model is derived from a standard
                  hydraulic simulator. An initial schedule is produced by a
                  descent method. Two-stage simulated annealing then produces
                  solutions in a few minutes. Iterative recalibration ensures
                  that the solution agrees closely with the results from a full
                  hydraulic simulation.}
}

@Article{McDermott2020nfl,
  author =       {James McDermott},
  title =        {When and Why Metaheuristics Researchers can Ignore "No Free
                  Lunch" Theorems},
  journal =      sncs,
  volume =       1,
  number =       60,
  pages =        {1--18},
  year =         2020,
  doi =          {10.1007/s42979-020-0063-3},
}

@Article{McG1992vrt,
  author =       McGeoch_CC,
  title =        {Analyzing Algorithms by Simulation: Variance Reduction
                  Techniques and Simulation Speedups},
  abstract =     {Although experimental studies have been widely applied to the
                  investigation of algorithm performance, very little attention
                  has been given to experimental method in this area. This is
                  unfortunate, since much can be done to improve the quality of
                  the data obtained; often, much improvement may be needed for
                  the data to be useful. This paper gives a tutorial discussion
                  of two aspects of good experimental technique: the use of
                  variance reduction techniques and simulation speedups in
                  algorithm studies.  In an illustrative study, application of
                  variance reduction techniques produces a decrease in variance
                  by a factor 1000 in one case, giving a dramatic improvement
                  in the precision of experimental results. Furthermore, the
                  complexity of the simulation program is improved from
                  $\Theta(m n/H_n)$ to $\Theta(m + n \log n)$ (where $m$ is
                  typically much larger than $n$), giving a much faster
                  simulation program and therefore more data per unit of
                  computation time. The general application of variance
                  reduction techniques is also discussed for a variety of
                  algorithm problem domains.},
  volume =       24,
  doi =          {10.1145/130844.130853},
  number =       2,
  journal =      acm-cs,
  year =         1992,
  keywords =     {experimental analysis of algorithms, move-to-front rule,
                  self-organizing sequential search, statistical analysis of
                  algorithms, transpose rule, variance reduction techniques},
  pages =        {195--212},
}

@Article{McG1998joc,
  author =       McGeoch_CC,
  title =        {Toward an Experimental Method for Algorithm Simulation},
  journal =      informsjoc,
  year =         1996,
  volume =       8,
  number =       1,
  pages =        {1--15},
  doi =          {10.1287/ijoc.8.1.1},
}

@Article{MckBecCon1979lhs,
  title =        {A Comparison of Three Methods for Selecting Values of Input
                  Variables in the Analysis of Output from a Computer Code},
  author =       {Michael D. McKay and Richard J. Beckman} #and# Conover,
  journal =      techms,
  year =         1979,
  number =       2,
  pages =        {239--245},
  volume =       21,
  alias =        "McKay1979",
  abstract =     {Two types of sampling plans are examined as alternatives to
                  simple random sampling in Monte Carlo studies. These plans
                  are shown to be improvements over simple random sampling with
                  respect to variance for a class of estimators which includes
                  the sample mean and the empirical distribution function.},
  publisher =    {American Statistical Association and American Society for
                  Quality},
  doi =          "10.2307/1268522"
}

@Article{MckBerMaiFic2018combining,
  title =        {Combining local preferences with multi-criteria decision
                  analysis and linear optimization to develop feasible energy
                  concepts in small communities},
  author =       {McKenna, Russell and Bertsch, Valentin and Mainzer, Kai and
                  Fichtner, Wolf},
  journal =      ejor,
  volume =       268,
  number =       3,
  pages =        {1092--1110},
  year =         2018,
}

@Article{Mckay2010,
  author =       {Mckay, Robert I. and Hoai, Nguyen Xuan and Whigham,
                  Peter Alexander and Shan, Yin} #and# ONeill,
  title =        "Grammar-based Genetic Programming: A Survey",
  journal =      gpem,
  volume =       11,
  number =       {3-4},
  month =        sep,
  year =         2010,
  pages =        {365--396},
  doi =          {10.1007/s10710-010-9109-y}
}

@Article{Meer2007,
  author =       {Klaus Meer},
  title =        "Simulated annealing versus {Metropolis} for a {TSP} instance",
  journal =      ipl,
  volume =       104,
  number =       6,
  year =         2007,
  pages =        "216--219"
}

@Article{MelDyeBlu2017neural,
  author =       {G{\'{a}}bor Melis and Chris Dyer and Phil Blunsom},
  title =        {On the State of the Art of Evaluation in Neural Language
                  Models},
  journal =      arxiv # {1807.02811},
  year =         2017,
  url =          {http://arxiv.org/abs/1707.05589},
}

@Article{MelNicSal2009facility,
  title =        {Facility location and supply chain management: {A} review},
  author =       {Melo, M. T.} #and# {Nickel, S.} #and# Saldanha-da-Gama,
  year =         2009,
  journal =      ejor,
  volume =       196,
  number =       2,
  pages =        {401--412},
  doi =          {10.1016/j.ejor.2008.05.007}
}

@Article{Men2008,
  author =       "Ole J. Mengshoel",
  title =        "Understanding the role of noise in stochastic local search:
                  Analysis and experiments",
  journal =      aij,
  volume =       172,
  number =       8,
  pages =        {955--990},
  year =         2008
}

@Article{MerCot2006sigevo,
  author =       Merelo_JJ #and# Cotta,
  title =        {Building bridges: the role of subfields in metaheuristics},
  journal =      sigevolution,
  year =         2006,
  volume =       1,
  number =       4,
  pages =        {9--15},
  doi =          {10.1145/1229735.1229737},
}

@Article{MerFre02:cs,
  author =       Merz #and# Freisleben,
  title =        {Memetic Algorithms for the Traveling Salesman
                  Problem},
  journal =      cs,
  year =         2001,
  volume =       13,
  number =       4,
  pages =        {297--345},
}

@Article{MerFre2000:tec,
  author =       Merz #and# Freisleben,
  title =        {Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem},
  journal =      tec,
  year =         2000,
  volume =       4,
  number =       4,
  pages =        {337--352}
}

@Article{MerKat2004ubqp,
  author =       Merz #and# "Kengo Katayama",
  title =        "Memetic algorithms for the unconstrained binary quadratic
                  programming problem",
  journal =      biosys,
  volume =       78,
  number =       1,
  pages =        "99--118",
  year =         2004,
  doi =          "10.1016/j.biosystems.2004.08.002"
}

@Article{MerMid2002:appi,
  author =       Merkle #and# Middendorf,
  title =        {Ant Colony Optimization with Global Pheromone
                  Evaluation for Scheduling a Single Machine},
  journal =      appi,
  year =         2003,
  volume =       18,
  number =       1,
  pages =        {105--111},
  alias =        {MerMid02:ai}
}

@Article{MerMid2002:ec,
  author =       Merkle #and# Middendorf,
  title =        "Modeling the Dynamics of Ant Colony Optimization",
  journal =      ec,
  volume =       10,
  number =       3,
  pages =        "235--262",
  year =         2002,
}

@Article{MerMidSch02:tec,
  author =       Merkle #and# Middendorf #and# Schmeck,
  title =        {Ant Colony Optimization for Resource-Constrained
                  Project Scheduling},
  journal =      tec,
  year =         2002,
  volume =       6,
  number =       4,
  pages =        {333--346}
}

@Article{Merz2002joh,
  author =       Merz #and# Freisleben,
  title =        {Greedy and Local Search Heuristics for Unconstrained
                  Binary Quadratic Programming},
  year =         2002,
  journal =      joh,
  volume =       8,
  number =       2,
  doi =          {10.1023/A:1017912624016},
  pages =        {197--213},
}

@Article{MesSilMelMir20152esa,
  author =       "Rafael G. Mesquita and Ricardo M. A. Silva and Carlos A. B. Mello and P\'{e}ricles B. C. Miranda",
  title =        "Parameter tuning for document image binarization using a racing algorithm",
  journal =      eswa,
  volume =       42,
  number =       5,
  pages =        {2593--2603},
  year =         2015,
  doi =          {10.1016/j.eswa.2014.10.039},
  keywords =     {irace},
}

@Article{MetRosRosTel53,
  author =       {N. Metropolis and A. W. Rosenbluth and M. N. Rosenbluth and A. Teller and E. Teller},
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  volume =       21,
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}

@Article{MeuDor2002:al,
  author =       Meuleau #and# Dorigo,
  title =        "Ant Colony Optimization and Stochastic Gradient Descent",
  volume =       8,
  number =       2,
  pages =        "103--121",
  journal =      al,
  year =         2002,
}

@Article{MeuRakWon2020iclrbb,
  author       = {Laurent Meunier and Herilalaina Rakotoarison and Pak{-}Kan
                  Wong and Baptiste Rozi{\`{e}}re and J{\'{e}}r{\'{e}}my Rapin}
                  #and# Teytaud_O #and# {Antoine Moreau} #and# Doerr_C,
  title        = {Black-Box Optimization Revisited: Improving Algorithm
                  Selection Wizards through Massive Benchmarking},
  journal      = arxiv # {2010.04542},
  year         = 2020,
  doi          = {10.48550/arXiv.2010.04542},
  keywords     = {Nevergrad, NGOpt}
}

@Article{MeuRakWon2022ngopt,
  author       = {Laurent Meunier and Herilalaina Rakotoarison and Pak{-}Kan
                  Wong and Baptiste Rozi{\`{e}}re and J{\'{e}}r{\'{e}}my Rapin}
                  #and# Teytaud_O #and# {Antoine Moreau} #and# Doerr_C,
  title        = {Black-Box Optimization Revisited: Improving Algorithm
                  Selection Wizards Through Massive Benchmarking},
  journal      = tec,
  year         = 2022,
  volume       = 26,
  number       = 3,
  pages        = {490--500},
  doi          = {10.1109/TEVC.2021.3108185},
  keywords     = {nevergrad, NGOpt}
}

@Article{Mhallah2014,
  author =       {R. {M'Hallah}},
  title =        {An iterated local search variable neighborhood descent hybrid heuristic for the total earliness tardiness permutation flow shop},
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  year =         2014,
  volume =       52,
  number =       13,
  pages =        {3802--3819}
}

@Article{MicDasRic1996cie,
  author =       Michalewicz #and# "Dipankar Dasgupta and Rodolphe
                  G. Le Riche" #and# Schoenauer,
  title =        "Evolutionary algorithms for constrained engineering
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  doi =          "10.1016/0360-8352(96)00037-X",
}

@Article{MicPriAmoYal2014:cor,
  author =       {Julien Michallet} #and# Prins #and# {Farouk Yalaoui and Gr{\'{e}}goire Vitry},
  title =        {Multi-start Iterated Local Search for the Periodic Vehicle Routing
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  year =         2014,
  volume =       41,
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@Article{Mie2014or,
  title =        {Survey of methods to visualize alternatives in multiple
                  criteria decision making problems},
  author =       Miettinen,
  journal =      orspec,
  volume =       36,
  number =       1,
  pages =        {3--37},
  year =         2014,
  doi =          "10.1007/s00291-012-0297-0"
}

@Article{MieEskRui2010nautilus,
  author       = Miettinen #and# {Eskelinen, Petri} #and# Ruiz_Francisco #and#
                  Luque_M,
  title        = {{NAUTILUS} method: {An} interactive technique in
                  multiobjective optimization based on the nadir point},
  journal      = ejor,
  year         = 2010,
  volume       = 206,
  number       = 2,
  pages        = {426--434},
  month        = oct,
  issn         = {0377-2217},
  shorttitle   = {{NAUTILUS} method},
  doi          = {10.1016/j.ejor.2010.02.041},
  abstract     = {Most interactive methods developed for solving multiobjective
                  optimization problems sequentially generate Pareto optimal or
                  nondominated vectors and the decision maker must always allow
                  impairment in at least one objective function to get a new
                  solution. The NAUTILUS method proposed is based on the
                  assumptions that past experiences affect decision makers'
                  hopes and that people do not react symmetrically to gains and
                  losses. Therefore, some decision makers may prefer to start
                  from the worst possible objective values and to improve every
                  objective step by step according to their preferences. In
                  NAUTILUS, starting from the nadir point, a solution is
                  obtained at each iteration which dominates the previous
                  one. Although only the last solution will be Pareto optimal,
                  the decision maker never looses sight of the Pareto optimal
                  set, and the search is oriented so that (s)he progressively
                  focusses on the preferred part of the Pareto optimal
                  set. Each new solution is obtained by minimizing an
                  achievement scalarizing function including preferences about
                  desired improvements in objective function values. NAUTILUS
                  is specially suitable for avoiding undesired anchoring
                  effects, for example in negotiation support problems, or just
                  as a means of finding an initial Pareto optimal solution for
                  any interactive procedure. An illustrative example
                  demonstrates how this new method iterates.},
  language     = {en},
  keywords     = {Reference point methods, Interactive methods, Multiple
                  objective programming, Pareto optimality, Preference
                  information}
}

@Article{MieMusSte2014nimbus,
  title =        {Interactive multiobjective optimization with {NIMBUS} for
                  decision making under uncertainty},
  author =       Miettinen #and# {Mustajoki, Jyri} #and# Stewart_TJ,
  journal =      orspec,
  volume =       36,
  number =       1,
  pages =        {39--56},
  year =         2014,
}

@Article{MilAnd2005repl,
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  year =         2004,
  doi =          {10.1016/j.fishres.2004.08.016}
}

@Article{Miller1956,
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  publisher =    {American Psychological Association},
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}

@Article{Min96auto,
  author =       {Steven Minton},
  title =        {Automatically configuring constraint satisfaction programs: A
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  volume =       1,
  number =       1,
  pages =        {7--43},
  doi =          {10.1007/BF00143877}
}

@Article{MinRuiCia08,
  author =       Minella #and# Ruiz_Ruben #and# Ciavotta,
  title =        {A Review and Evaluation of Multiobjective Algorithms
                  for the Flowshop Scheduling Problem},
  journal =      informsjoc,
  volume =       20,
  number =       3,
  pages =        {451--471},
  year =         2008,
  alias =        {GerardoMinella,MinRuiCia08mfsp}
}

@Article{MisAfsLar2022rximo,
  author =       {Misitano, Giovanni and Afsar, Bekir and Larraga, Giomara}
                  #and# Miettinen,
  title =        {Towards explainable interactive multiobjective optimization:
                  {R-XIMO}},
  journal =      jaamas,
  year =         2022,
  volume =       36,
  number =       42,
  doi =          {10.1007/s10458-022-09577-3},
}

@Article{MisKuz2018,
  title =        {Investigating some strategies for construction of
                  initial populations in genetic algorithms},
  author =       Misevicius_A #and# {Kuznecovait{\.e}, Dovil{\.e}},
  journal =      {Computational Science and Techniques},
  volume =       5,
  number =       1,
  pages =        {560--573},
  year =         2018
}

@Article{Misevicius2003,
  author =       Misevicius_A,
  title =        {Genetic Algorithm Hybridized with Ruin and Recreate Procedure: Application to the Quadratic Assignment Problem},
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  year =         2003,
  volume =       16,
  number =       {5--6},
  pages =        {261--268}
}

@Article{Misevicius2003:inf,
  title =        {A modified simulated annealing algorithm for the quadratic assignment problem},
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  number =       4,
  pages =        {497--514},
  year =         2003,
  publisher =    ios
}

@Article{MitMurPal2002unsupervised,
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  volume =       24,
  number =       3,
  pages =        {301--312},
  doi =          {10.1109/34.990133}
}

@Article{MizKuzPla2018,
  author =       Misevicius_A #and# {Kuznecovait{\.e}, Dovil{\.e} and
                  J{\={u}}rat{\.e} Platu{\v{z}}ien{\.e}},
  title =        {Some Further Experiments with Crossover Operators for Genetic Algorithms},
  journal =      inf,
  volume =       29,
  number =       3,
  year =         2018,
  pages =        {499--516}
}

@Article{MlaHan1997:cor,
  author =       Mladenovic #and# Hansen_P,
  title =        {Variable Neighborhood Search},
  journal =      cor,
  year =         1997,
  volume =       24,
  number =       11,
  pages =        {1097--1100}
}

@Article{MniKavSil2015human,
  title =        {Human-level control through deep reinforcement learning},
  author =       {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and
                  Rusu, Andrei A. and Veness, Joel and Bellemare, Marc G. and
                  Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K.
                  and Ostrovski, Georg and others},
  journal =      nature,
  volume =       518,
  number =       7540,
  pages =        529,
  year =         2015,
  publisher =    {Nature Publishing Group}
}

@Article{MocTieZil1978,
  author       = Mockus_J #and# {Tiesis, Vytautas and Zilinskas, Antanas},
  title        = {The application of bayesian methods for seeking the extremum},
  journal      = {Towards global optimization},
  year         = 1978,
  pages        = {117--129},
  annote       = {Proposed Bayesian optimization (but later than
                  \cite{Mockus1975})}
}

@Article{MolSanHer2009gdominance,
  author =       Molina_J #and# {Luis V. Santana and Alfredo
                  G. Hern\'{a}ndez-D{\'i}az} #and# Coello #and# {Rafael Caballero},
  title =        {{g}-Dominance: Reference point based dominance for
                  Multiobjective Metaheuristics},
  journal =      ejor,
  volume =       197,
  number =       2,
  pages =        {685--692},
  month =        sep,
  year =         2009,
  doi =          "10.1016/j.ejor.2008.07.015",
  annote = "Proposed $g$-NSGA-II"
}

@Article{MonAydStu2011soco,
  author =       MontesDeOca #and# Dogan #and# Stuetzle,
  title =        {An Incremental Particle Swarm for Large-Scale
                  Continuous Optimization Problems: An Example of
                  Tuning-in-the-loop (Re)Design of Optimization
                  Algorithms},
  journal =      soco,
  year =         2011,
  volume =       15,
  number =       11,
  pages =        {2233--2255},
  doi =          {10.1007/s00500-010-0649-0}
}

@Article{MonFraLiu2018bridge,
  title =        {Multi-criteria robust optimization framework for bridge
                  adaptation under climate change},
  author =       {Mondoro, Alysson and Frangopol, Dan M. and Liu, Liang},
  journal =      {Structural Safety},
  volume =       74,
  pages =        {14--23},
  year =         2018,
}

@Article{MonGamRiz2005,
  author =       Montemanni_R #and# Gambardella #and# {A. E. Rizzoli and
                  A. V. Donati},
  title =        {Ant colony system for a dynamic vehicle routing problem},
  journal =      jco,
  year =         2005,
  volume =       10,
  pages =        {327--343},
  alias =        {Mon++05},
}

@Article{MonRandHen08:bias,
  author =       Montgomery #and# Randall_M #and# Hendtlass_T,
  title =        {Solution bias in ant colony optimisation: {Lessons}
                  for selecting pheromone models},
  journal =      cor,
  volume =       35,
  number =       9,
  year =         2008,
  pages =        {2728--2749},
  doi =          {10.1016/j.cor.2006.12.014},
}

@Article{MonRifNev2014asoc,
  author =       Montero #and# Riff #and# {Neveu, Bertrand},
  title =        "A Beginner's Buide to Tuning Methods",
  journal =      asoco,
  volume =       17,
  pages =        {39--51},
  year =         2014,
  doi =          {10.1016/j.asoc.2013.12.017}
}

@Article{MonStuBirDor2009tec,
  author =       MontesDeOca #and# Stuetzle #and# Birattari #and#
                  Dorigo,
  title =        "Frankenstein's {PSO}: A Composite Particle Swarm
                  Optimization Algorithm",
  journal =      tec,
  volume =       13,
  number =       5,
  year =         2009,
  pages =        {1120--1132},
  doi =          {10.1109/TEVC.2009.2021465},
}

@Article{MonVenSli2000:fgcs,
  author =       Monmarche_N #and# {G. Venturini and M. Slimane},
  title =        "On how {\em pachycondyla apicalis} ants suggest a
                  new search algorithm",
  journal =      fgcs,
  year =         2000,
  volume =       16,
  number =       8,
  pages =        {937--946}
}

@Article{Mor1993sim,
  title =        {Simulation of an adaptive behavior mechanism in an
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  volume =       23,
  number =       1,
  journal =      tsmc,
  author =       {Morgan, Peter D.},
  year =         1993,
  pages =        {65--76},
}

@Article{Mor80,
  title =        {Reducing the size of the nondominated set: Pruning
                  by clustering},
  author =       Morse,
  journal =      cor,
  volume =       7,
  number =       {1-2},
  pages =        {55--66},
  year =         1980,
}

@Article{MorDesLod2021machine,
  title =        {Machine-learning--based column selection for column
                  generation},
  author =       {Morabit, Mouad} #and# {Desaulniers, Guy} #and# Lodi,
  journal =      ts,
  volume =       55,
  number =       4,
  pages =        {815--831},
  year =         2021,
  publisher =    {INFORMS},
  keywords =     "graph neural networks"
}

@Article{MorGagGra09:ejor,
  author =       {Sara Morin} #and# Gagne_C #and# {Marc Gravel},
  title =        {Ant colony optimization with a specialized pheromone
                  trail for the car-sequencing problem},
  volume =       197,
  doi =         {10.1016/j.ejor.2008.03.033},
  abstract =     { This paper studies the learning process in an ant
                  colony optimization algorithm designed to solve the
                  problem of ordering cars on an assembly line
                  (car-sequencing problem). This problem has been
                  shown to be {NP-hard} and evokes a great deal of
                  interest among practitioners. Learning in an ant
                  algorithm is achieved by using an artificial
                  pheromone trail, which is a central element of this
                  metaheuristic. Many versions of the algorithm are
                  found in literature, the main distinction among them
                  being the management of the pheromone
                  trail. Nevertheless, few of them seek to perfect
                  learning by modifying the internal structure of the
                  trail. In this paper, a new pheromone trail
                  structure is proposed that is specifically adapted
                  to the type of constraints in the car-sequencing
                  problem. The quality of the results obtained when
                  solving three sets of benchmark problems is superior
                  to that of the best solutions found in literature
                  and shows the efficiency of the specialized trail.},
  number =       3,
  journal =      ejor,
  year =         2009,
  keywords =     {Ant colony {optimization,Car-sequencing}
                  {problem,Pheromone} {trail,Scheduling}},
  pages =        {1185--1191}
}

@Article{MorMerLarMilTor09,
  author =       {Mora, A. M.} #and# Merelo_JJ #and# Jimenez_Laredo  #and#
                  {Millan, C.} #and# {Torrecillas, J.},
  title =        {{CHAC}, a {MOACO} algorithm for computation of bi-criteria
                  military unit path in the battlefield: Presentation and first
                  results},
  journal =      ijis,
  volume =       24,
  number =       7,
  publisher =    {Wiley Subscription Services, Inc., A Wiley Company},
  pages =        {818--843},
  year =         2009
}

@Article{MorMit1995,
  title =        {Exploratory designs for computational experiments},
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  year =         1995,
  number =       3,
  pages =        {381--402},
  volume =       43,
  doi =          {10.1016/0378-3758(94)00035-T},
  keywords =     {Bayesian prediction},
  alias =        "Morris1995"
}

@Article{MosFon1990,
  title =        {Stochastic Versus Deterministic Update in Simulated Annealing},
  author =       Moscato #and# Fontanari_JF,
  journal =      pla,
  volume =       146,
  number =       4,
  pages =        {204--208},
  year =         1990,
  publisher =    elsevier
}

@Article{MotMurOls1991,
  author =       {John Mote and Ishwar Murthy and David L. Olson},
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  journal =      ejor,
  volume =       53,
  number =       1,
  pages =        {81--92},
  year =         1991,
  doi =          {10.1016/0377-2217(91)90094-C},
}

@Article{MotOlsVen1988moprog,
  author =       {John Mote and David L. Olson and M. A. Venkataramanan},
  doi =          {10.1016/0895-7177(88)90085-4},
  year =         1988,
  publisher =    {Elsevier {BV}},
  volume =       10,
  number =       10,
  pages =        {719--729},
  title =        {A comparative multiobjective programming study},
  journal =      mcm,
  keywords = "artificial DM, interactive",
  annote =       "The purpose of this study was to systematically evaluate a
                  number of multiobjective programming concepts relative to
                  reflection of utility, assurance of nondominated solutions
                  and practicality for larger problems using conventional
                  software. In the problem used, the nonlinear simulated DM
                  utility function applied resulted in a nonextreme point
                  solution. Very often, the preferred solution could end up
                  being an extreme point solution, in which case the techniques
                  relying upon LP concepts would work as well if not better
                  than utilizing constrained objective attainments. The point
                  is that there is no reason to expect linear or near linear
                  utility."
}

@Article{MouDevHen2012,
  author = 	 {S\'ebastien Mouthuy} #and# Deville_Y #and# Hentenryck,
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  volume = 	 17,
  number = 2,
  pages = 	 {87--122},
  doi = {10.1007/s10601-011-9114-7}
}

@Article{MouKesDha2019,
  author =       {Mousin, Lucien} #and# MarmionKs #and# Dhaenens,
  title =        "Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop Scheduling Problem",
  journal =      arxiv # {1903.09035},
  year =         2019,
  url =          {http://arxiv.org/abs/1903.09035}
}

@Article{MouPetMcC2014mopso,
  author       = {Al Moubayed, Noura and Petrovski, Andrei} #and# McCall_J,
  title        = {{$D^2MOPSO$}: {MOPSO} based on decomposition and dominance
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                  solution spaces},
  journal      = ec,
  year         = 2014,
  volume       = 22,
  number       = 1,
  pages        = {47--77}
}

@Article{MouSlo1998:jgo,
  author =       Mousseau #and# Slowinski,
  year =         1998,
  title =        "Inferring an {ELECTRE TRI} model from assignment examples",
  journal =      jgo,
  volume =       12,
  number =       2,
  pages =        "157--174",
}

@Article{MueSba2012ecj,
  title =        {Energy Landscapes of Atomic Clusters as Black Box
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  number =       4,
  pages =        {543--573},
  volume =       20,
  doi =          {10.1162/EVCO_a_00086},
  publisher =    {MIT Press},
  alias =        "Mueller2012"
}

@Article{Muhlenbein93,
  author =       Muhlenbein #and# Schlierkamp-Voosen,
  title =        "Predictive models for the breeder genetic algorithm",
  journal =      ec,
  year =         1993,
  volume =       1,
  number =       1,
  pages =        {25--49},
  note =         notreadyet,
  keywords =     "crossover, intermediate, line"
}

@Article{MunSmi2020ec,
  author =       Munoz_MA #and# Smith-Miles_K,
  title =        {Generating New Space-Filling Test Instances for Continuous
                  Black-Box Optimization},
  doi =          {10.1162/evco_a_00262},
  year =         2020,
  month =        sep,
  publisher =    {{MIT} Press},
  volume =       28,
  number =       3,
  pages =        {379--404},
  journal =      ec,
}

@Article{MunSunKirHal2015sel,
  title =        {Algorithm selection for black-box continuous optimization
                  problems: a survey on methods and challenges},
  author =       Munoz_MA #and# {Sun, Yuan and Kirley, Michael and
                  Halgamuge, Saman K.},
  journal =      is,
  volume =       317,
  pages =        {224--245},
  year =         2015,
}

@Article{MunVilBaaSmi2018ismlc,
  author       = Munoz_MA #and# {Villanova, Laura and Baatar, Davaatseren}
                  #and# Smith-Miles_K,
  title        = {Instance Spaces for Machine Learning Classification},
  journal      = ml,
  year         = 2018,
  volume       = 107,
  number       = 1,
  pages        = {109--147},
  doi          = {10.1007/s10994-017-5629-5}
}

@Article{NagKob2013,
  author =       {Yuichi Nagata and Shigenobu Kobayashi},
  title =        {A Powerful Genetic Algorithm Using Edge Assembly Crossover
                  for the Traveling Salesman Problem},
  journal =      informsjoc,
  year =         2013,
  volume =       25,
  number =       2,
  pages =        {346--363},
  doi =          "10.1287/ijoc.1120.0506",
  keywords =     {TSP, EAX, evolutionary algorithms},
  abstract =     {This paper presents a genetic algorithm (GA) for solving the
                  traveling salesman problem (TSP). To construct a powerful GA,
                  we use edge assembly crossover (EAX) and make substantial
                  enhancements to it: (i) localization of EAX together with its
                  efficient implementation and (ii) the use of a local search
                  procedure in EAX to determine good combinations of building
                  blocks of parent solutions for generating even better
                  offspring solutions from very high-quality parent
                  solutions. In addition, we develop (iii) an innovative
                  selection model for maintaining population diversity at a
                  negligible computational cost. Experimental results on
                  well-studied TSP benchmarks demonstrate that the proposed GA
                  outperforms state-of-the-art heuristic algorithms in finding
                  very high-quality solutions on instances with up to 200,000
                  cities. In contrast to the state-of-the-art TSP heuristics,
                  which are all based on the Lin-Kernighan (LK) algorithm, our
                  GA achieves top performance without using an LK-based
                  algorithm.}
}

@Article{NagRosMar2019:eo,
  title   = {High-performing heuristics to minimize flowtime in no-idle permutation flowshop},
  author  = {Marcelo S. Nagano and  Fernando L. Rossi and  N{\'a}dia J. Martarelli},
  journal = eo,
  volume  = 51,
  number  = 2,
  pages   = {185--198},
  year    = 2019
}

@Article{NagSol2012,
  author =       {Yuichi Nagata and David Soler},
  title =        {A New Genetic Algorithm for the Asymmetric {TSP}},
  journal =      eswa,
  year =         2012,
  volume =       39,
  number =       10,
  pages =        {8947--8953}
}

@Article{NalOliHerSud2019,
  title =        {On the Analysis of Trajectory-Based Search Algorithms:
                  When is it Beneficial to Reject Improvements?},
  author =       Nallaperuma #and# Oliveto_PS #and# {Heredia, Jorge P{\'e}rez}
                  #and# Sudholt,
  journal =      algorithmica,
  volume =       81,
  number =       2,
  pages =        {858--885},
  year =         2019,
  publisher =    springer
}

@Article{NanShaIsh2020reverse,
  title =        {Reverse strategy for non-dominated archiving},
  author =       {Nan, Yang and Shang, Ke} #and# Ishibuchi #and# {He, Linjun},
  journal =      ieee-access,
  year =         2020,
  pages =        {119458--119469},
  volume =       8,
  publisher =    {IEEE}
}

@Article{NarSetTan2016sc,
  author =       {Narukawa, Kaname and Setoguchi, Yu and Tanigaki, Yuki and
                  Olhofer, Markus and Sendhoff, Bernhard} #and# Ishibuchi,
  title =        {Preference representation using {Gaussian} functions on a
                  hyperplane in evolutionary multi-objective optimization},
  journal =      soco,
  year =         2016,
  volume =       20,
  number =       7,
  pages =        {2733--2757},
  month =        jul,
  doi =          {10.1007/s00500-015-1674-9},
}

@Article{NasVar2011optimx,
  title =        {Unifying Optimization Algorithms to Aid Software System
                  Users: \rpackage{optimx} for \proglang{R}},
  author =       {Nash, John and Varadhan, Ravi},
  journal =      jss,
  year =         2011,
  number =       9,
  pages =        {1--14},
  volume =       43,
  alias =        "Nash2011"
}

@Article{NawEnsHam83,
  author =       Nawaz #and# Enscore #and# Ham_I,
  title =        "A Heuristic Algorithm for the $m$-Machine, $n$-Job
                  Flow-Shop Sequencing Problem",
  journal =      omega,
  year =         1983,
  volume =       11,
  number =       1,
  pages =        {91--95},
  keywords =      {NEH heuristic},
}

@Article{NebLopGarCoe2023automopso,
  author       = Nebro #and# Lopez-Ibanez #and# Garcia-Nieto #and# Coello,
  title        = {On the automatic design of multi-objective particle swarm
                  optimizers: experimentation and analysis},
  journal      = swarm,
  year         = 2023,
  doi          = {10.1007/s11721-023-00227-2},
  abstract     = {Research in multi-objective particle swarm optimizers
                  (MOPSOs) progresses by proposing one new MOPSO at a time. In
                  spite of the commonalities among different MOPSOs, it is
                  often unclear which algorithmic components are crucial for
                  explaining the performance of a particular MOPSO
                  design. Moreover, it is expected that different designs may
                  perform best on different problem families and identifying a
                  best overall MOPSO is a challenging task. We tackle this
                  challenge here by: (1) proposing AutoMOPSO, a flexible
                  algorithmic template for designing MOPSOs with a design space
                  that can instantiate thousands of potential MOPSOs; and (2)
                  searching for good-performing MOPSO designs given a family of
                  training problems by means of an automatic configuration tool
                  (irace). We apply this automatic design methodology to
                  generate a MOPSO that significantly outperforms two
                  state-of-the-art MOPSOs on four well-known bi-objective
                  problem families. We also identify the key design choices and
                  parameters of the winning MOPSO by means of
                  ablation. AutoMOPSO is publicly available as part of the
                  jMetal framework.}
}

@Article{NebLunAlb2008abyss,
  author =       Nebro #and# {F. Luna}#and# Alba_E #and# Dorronsoro #and#
                  Durillo_JJ #and# {A. Beham},
  title =        "{AbYSS}: Adapting Scatter Search to Multiobjective Optimization",
  journal =      tec,
  volume =       12,
  number =       4,
  month =        aug,
  year =         2008,
  pages =        "439--457",
  doi =          "10.1109/TEVC.2007.913109"
}

@Article{NerCot2012,
  author =       Nerri #and# Cotta,
  title =        "Memetic algorithms and memetic computing optimization:
                  A literature review",
  journal =      swevo,
  year =         2012,
  volume =       2,
  pages =        {1--14},
  doi =          {10.1016/j.swevo.2011.11.003},
}

@Article{NeuSudWit2009:swarm,
  author =       Neumann_F #and# Sudholt #and# Witt,
  title =        "Analysis of different {MMAS} {ACO} algorithms on
                  unimodal functions and plateaus",
  journal =      swarm,
  year =         2009,
  volume =       3,
  number =       1,
  pages =        {35--68}
}

@Article{NeuWit2006:eccc,
  author =       Neumann_F #and# Witt,
  title =        "Runtime Analysis of a Simple Ant Colony Optimization
                  Algorithm",
  journal =      eccc,
  volume =       13,
  number =       084,
  year =         2006,
}

@Article{NewSim1976cacm,
  author =       {Newell, Allen and Simon, Herbert A.},
  title =        {Computer Science as Empirical Inquiry: Symbols and Search},
  year =         1976,
  volume =       19,
  number =       3,
  issn =         {0001-0782},
  doi =          {10.1145/360018.360022},
  abstract =     {Computer science is the study of the phenomena surrounding
                  computers. The founders of this society understood this very
                  well when they called themselves the Association for
                  Computing Machinery. The machine-not just the hardware, but
                  the programmed, living machine-is the organism we study.},
  journal =      cacm,
  month =        mar,
  pages =        {113--126},
  numpages =     14,
  keywords =     {cognition, Turing, search, problem solving, symbols,
                  heuristics, list processing, computer science, artificial
                  intelligence, science, empirical}
}

@Article{NguPriPro2012:eaai,
  author =       {Viet-Phuong Nguyen} #and# Prins #and# {Caroline Prodhon},
  title =        {A Multi-start Iterated Local Search with Tabu List and Path Relinking
               for the Two-echelon Location-routing Problem},
  journal =      eaai,
  year =         2012,
  volume =       25,
  number =       1,
  pages =        {56--71}
}

@Article{NguReiRig2014,
  title =        "A review on simulation-based optimization methods applied to
                  building performance analysis",
  journal =      "Applied Energy",
  volume =       113,
  pages =        "1043--1058",
  year =         2014,
  doi =          "10.1016/j.apenergy.2013.08.061",
  author =       "Anh-Tuan Nguyen and Sigrid Reiter and Philippe Rigo",
}

@Article{NguYanBra2012:swec,
  author =       {Trung Thanh Nguyen and Shengxiang Yang} #and# Branke,
  title =        {Evolutionary Dynamic Optimization: A Survey of the State of the Art},
  journal =      swevo,
  year =         2012,
  volume =       6,
  pages =        {1--24}
}

@Article{NguZhaJohChe14:ec,
  author =       {Su Nguyen} #and#  Zhang_Mengjie #and# {Mark Johnston} #and# Tan_KC,
  title =        {Genetic Programming for Evolving Due-Date Assignment Models in Job Shop Environments},
  journal =      ec,
  year =         2014,
  volume =       22,
  number =       1,
  pages =        {105--138}
}

@Article{NguZhaJohChe14:tec,
  author =       {Su Nguyen} #and# Zhang_Mengjie #and# {Mark Johnston} #and# Tan_KC,
  title =        {Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming},
  journal =      tec,
  year =         2014,
  volume =       18,
  number =       2,
  pages =        {193--208}
}

@Article{NigAkgGen++2017:aij,
  author = 	 {Peter Nightingale and \"Ozgu\"ur Akg\"un} #and# Gent_IP #and# {Christopher Jefferson and Ian Miguel and Patrick Spracklen},
  title = 	 {Automatically Improving Constraint Models in {Savile} {Row}},
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  year = 	 2017,
  volume = 	 251,
  pages = 	 {35--61}
}

@Article{NinYou2019optunc,
  author       = {Chao Ning and Fengqi You},
  title        = {Optimization under uncertainty in the era of big data and
                  deep learning: When machine learning meets mathematical
                  programming},
  journal      = compchemeng,
  year         = 2019,
  volume       = 125,
  pages        = {434--448},
  doi          = {10.1016/j.compchemeng.2019.03.034},
  keywords     = {Data-driven optimization, Decision making under uncertainty,
                  Big data, Machine learning, Deep learning},
  abstract     = {This paper reviews recent advances in the field of
                  optimization under uncertainty via a modern data lens,
                  highlights key research challenges and promise of data-driven
                  optimization that organically integrates machine learning and
                  mathematical programming for decision-making under
                  uncertainty, and identifies potential research
                  opportunities. A brief review of classical mathematical
                  programming techniques for hedging against uncertainty is
                  first presented, along with their wide spectrum of
                  applications in Process Systems Engineering. A comprehensive
                  review and classification of the relevant publications on
                  data-driven distributionally robust optimization, data-driven
                  chance constrained program, data-driven robust optimization,
                  and data-driven scenario-based optimization is then
                  presented. This paper also identifies fertile avenues for
                  future research that focuses on a closed-loop data-driven
                  optimization framework, which allows the feedback from
                  mathematical programming to machine learning, as well as
                  scenario-based optimization leveraging the power of deep
                  learning techniques. Perspectives on online learning-based
                  data-driven multistage optimization with a
                  learning-while-optimizing scheme are presented.}
}

@Article{NisTanSugMiy2019item,
  title =        {Item listing optimization for e-commerce websites based on
                  diversity},
  author =       {Nishimura, Naoki and Tanahashi, Kotaro and Suganuma, Koji and
                  Miyama, Masamichi J. and Ohzeki, Masayuki},
  journal =      {Frontiers in Computer Science},
  volume =       1,
  pages =        2,
  year =         2019,
  publisher =    {Frontiers},
  keywords = "Quantum Annealing"
}

@Article{Nitivatta96,
  author =       Nitivattananon #and# Sadowski #and# Quimpo,
  title =        {Optimization of Water Supply System Operation},
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  volume =       122,
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@Article{NogPinSub2017:ol,
  author =       {Bruno Nogueira and Rian G. S. Pinheiro and Anand Subramanian},
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  journal =      ol,
  year =         2018,
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  number =       3,
  pages =        "567--583",
  doi =          "10.1007/s11590-017-1128-7",
}

@Article{NosAltBank2015promoting,
  doi =          {10.1126/science.aab2374},
  year =         2015,
  month =        jun,
  publisher =    {American Association for the Advancement of Science ({AAAS})},
  volume =       348,
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  pages =        {1422--1425},
  author =       {B. A. Nosek and G. Alter and G. C. Banks and D. Borsboom and
                  S. D. Bowman and S. J. Breckler and S. Buck and
                  C. D. Chambers and G. Chin and G. Christensen and
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                  M. Humphreys and J. Ishiyama and D. Karlan and A. Kraut and
                  A. Lupia and P. Mabry and T. Madon and N. Malhotra and
                  E. Mayo-Wilson and M. McNutt and E. Miguel and E. L. Paluck
                  and U. Simonsohn and C. Soderberg and B. A. Spellman and
                  J. Turitto and G. VandenBos and S. Vazire and
                  E. J. Wagenmakers and R. Wilson and T. Yarkoni},
  title =        {Promoting an open research culture},
  journal =      Science
}

@Article{NosEbeHav2018preregistration,
  author =       {Nosek, Brian A. and Ebersole, Charles R. and DeHaven,
                  Alexander C. and Mellor, David T.},
  title =        {The Preregistration Revolution},
  volume =       115,
  issn =         {0027-8424, 1091-6490},
  doi =          {10.1073/pnas.1708274114},
  language =     {en},
  number =       11,
  journal =      pnas,
  month =        mar,
  year =         2018,
  pages =        {2600--2606},
  abstract =     {Progress in science relies in part on generating hypotheses
                  with existing observations and testing hypotheses with new
                  observations. This distinction between postdiction and
                  prediction is appreciated conceptually but is not respected
                  in practice. Mistaking generation of postdictions with
                  testing of predictions reduces the credibility of research
                  findings. However, ordinary biases in human reasoning, such
                  as hindsight bias, make it hard to avoid this mistake. An
                  effective solution is to define the research questions and
                  analysis plan before observing the research outcomes--a
                  process called preregistration. Preregistration distinguishes
                  analyses and outcomes that result from predictions from those
                  that result from postdictions. A variety of practical
                  strategies are available to make the best possible use of
                  preregistration in circumstances that fall short of the ideal
                  application, such as when the data are preexisting. Services
                  are now available for preregistration across all disciplines,
                  facilitating a rapid increase in the practice. Widespread
                  adoption of preregistration will increase distinctiveness
                  between hypothesis generation and hypothesis testing and will
                  improve the credibility of research findings.},
}

@Article{NouAnd1998,
  title =        {A Comparison of Simulated Annealing Cooling Strategies},
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  publisher =    iop
}

@Article{NowSmu1996:ms,
  author =       {Nowicki, Eugeniusz and Smutnicki, Czeslaw},
  title =        {A Fast Taboo Search Algorithm for the Job Shop Problem},
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  volume =       42,
  number =       6,
  pages =        {797--813}
}

@Article{NowSmu1996ejor,
  title   = {A fast tabu search algorithm for the permutation flow-shop problem},
  author  = {Nowicki, Eugeniusz and Smutnicki, Czes{\l}aw},
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  number  = 1,
  pages   = {160--175},
  year    = 1996
}

@Article{OSC2015estimating,
  title =        {Estimating the reproducibility of psychological science },
  author =       {{Open Science Collaboration}},
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  year =         2015,
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}

@Article{OchVer2018,
  title =        {Mapping the global structure of {TSP} fitness landscapes},
  author =       Ochoa #and# Veerapen,
  journal =      joh,
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  number =       3,
  pages =        {265--294},
  year =         2018,
  publisher =    springer
}

@Article{OddCesPolSmi2008,
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  journal =      eaai,
  year =         2008,
  volume =       21,
  number =       5,
  pages =        {683--690}
  }

@Article{OddCesPolSmi2010,
  author =        Oddi_A #and# Cesta_A #and# Policella #and# Smith_SF,
  title =        {Iterative Flattening Search for Resource Constrained Scheduling},
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  year =         2010,
  volume =       21,
  number =       1,
  pages =        {17--30}
  }

@Article{OgbuSmith1990,
  author =       Ogbu_FA #and# Smith_DK,
  title =        {The Application of the Simulated Annealing Algorithm to the Solution of the {n/m/C} Max Flowshop Problem},
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}

@Article{OhlTho07tsptw_sa,
  author =       Ohlmann #and# Thomas_BW,
  title =        {A Compressed-Annealing Heuristic for the Traveling
                  Salesman Problem with Time Windows},
  journal =      informsjoc,
  volume =       19,
  number =       1,
  year =         2007,
  issn =         {1526-5528},
  pages =        {80--90},
 doi =         {10.1287/ijoc.1050.0145},
  publisher =    informs,
  address =      {Institute for Operations Research and the Management
                  Sciences (INFORMS), Linthicum, Maryland, USA},
  pdf =          {OhlTho07SAonTSPTW.pdf}
}

@Article{OliHeYao2007,
  title =        {Time complexity of evolutionary algorithms for combinatorial
                  optimization: A decade of results},
  author =       Oliveto_PS #and# {He, Jun} #and# Yao_Xin,
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  volume =       4,
  number =       3,
  pages =        {281--293},
  year =         2007,
  publisher =    springer
}

@Article{OliWit2015,
  author =       Oliveto_PS #and# Witt,
  title =        {Improved time complexity analysis of the Simple Genetic Algorithm},
  journal =      tcs,
  volume =       605,
  pages =        {21--41},
  year =         2015,
  doi =          {10.1016/j.tcs.2015.01.002}
}

@Article{Ols1992review,
  author =       {Olson, David L.},
  title =        {Review of Empirical Studies in Multiobjective Mathematical
                  Programming: Subject Reflection of Nonlinear Utility and
                  Learning},
  journal =      ds,
  volume =       23,
  number =       1,
  pages =        {1--20},
  year =         1992,
  keywords =     {Decision Analysis, Human Information Processing, Linear
                  Programming},
  doi =          {10.1111/j.1540-5915.1992.tb00374.x},
  abstract =     {Multiple objective programming provides a means of
                  aiding decision makers facing complex decisions where
                  trade-offs among conflicting objectives must be
                  reconciled. Interactive multiobjective programming provides a
                  means for decision makers to learn what these trade-offs
                  involve, while the mathematical program generates solutions
                  that seek improvement of the implied utility of the decision
                  maker. A variety of multiobjective programming techniques
                  have been presented in the multicriteria decision-making
                  literature. This study reviews published studies with human
                  subjects where some of these techniques were applied. While
                  all of the techniques have the ability to support decision
                  makers under conditions of multiple objectives, a number of
                  features in applying these systems have been tested by these
                  studies. A general evolution of techniques is traced,
                  starting with methods relying upon linear combinations of
                  value, to more recent methods capable of reflecting nonlinear
                  trade-offs of value. Support of nonlinear utility and
                  enhancing decision-maker learning are considered.},
}

@Article{OlsLok2013joh,
  author =       {Roland Olsson} #and# Lokketangen,
  title =        {Using Automatic Programming to Generate State-of-the-art
                  Algorithms for Random 3-{SAT}},
  journal =      joh,
  volume =       19,
  number =       5,
  year =         2013,
  pages =        {819--844},
  doi =          {10.1007/s10732-013-9226-x},
  annote =       "Uses evolution but it is not genetic programming, nor
                  grammatical evolution."
}

@Article{Oltean05,
  author =       {Mihai Oltean},
  title =        {Evolving Evolutionary Algorithms Using Linear Genetic
                  Programming},
  journal =      ec,
  year =         2005,
  volume =       13,
  number =       3,
  pages =        {387--410},
  doi =          {10.1162/1063656054794815}
}

@Article{OneRya2001tec,
  title =        {Grammatical Evolution},
  author =       ONeill #and# {Ryan, Conor},
  journal =      tec,
  volume =       5,
  number =       4,
  pages =        {349--358},
  year =         2001,
}

@Article{Ormsbee89,
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  volume =       115,
  number =       2,
  pages =        {148--164},
  note =         notreadyet,
}

@Article{Ormsbee94,
  author =       Ormsbee #and# Lansey,
  title =        {Optimal Control of Water Supply Pumping Systems},
  journal =      jwrpm,
  year =         1994,
  volume =       120,
  number =       2,
  pages =        {237--252},
}

@Article{Ormsbee95,
  author =       Ormsbee #and# Reddy,
  title =        {Nonlinear Heuristic for Pump Operations},
  journal =      jwrpm,
  volume =       121,
  number =       4,
  pages =        {302--309},
  date =         "1995-07/1995-08",
  year =         1995,
  month =        jul # " / " # aug,
}

@Article{OroJac2002,
  title =        {Analysis of Static Simulated Annealing Algorithms},
  author =       Orosz_JE #and# Jacobson_SH,
  journal =      jota,
  volume =       115,
  number =       1,
  pages =        {165--182},
  year =         2002,
  publisher =    springer,
}

@Article{OsmPot89,
  author =       Osman #and# Potts,
  title =        {Simulated Annealing for Permutation Flow-Shop
                  Scheduling},
  journal =      omega,
  year =         1989,
  volume =       17,
  number =       6,
  pages =        {551--557}
}

@Article{OwMor1988:ijpr,
  author       = {Ow, P. S. and Morton, T. E.},
  title        = {Filtered Beam Search in Scheduling},
  journal      = ijpr,
  year         = 1988,
  volume       = 26,
  pages        = {297--307},
  annote       = {Proposed beam search},
  alias        = {OwMor88}
}

@Article{OzeKar2007:jors,
  author =       Ozerol #and# Karasakal,
  title =        "Interactive outranking approaches for multicriteria
                  decision-making problems with imprecise information",
  journal =      jors,
  year =         2007,
  volume =       59,
  pages =        {1253--1268}
}

@Article{PadRin1991siamr,
  title   = {A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems},
  author  = {Padberg, Manfred and Rinaldi, Giovanni},
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  volume  = 33,
  number  = 1,
  pages   = {60--100},
  year    = 1991
}

@Article{PagStu2016,
  author =     Pagnozzi #and# Stuetzle,
  title  =     {Speeding up Local Search for the Insert Neighborhood in the Weighted Tardiness Permutation Flowshop Problem},
  journal=     ol,
  year   =     2017,
  volume =     11,
  pages  =     {1283--1292},
  doi    =     {10.1007/s11590-016-1086-5}
}

@Article{PagStu2019:ejor,
  author       = Pagnozzi #and# Stuetzle,
  title        = {Automatic Design of Hybrid Stochastic Local Search Algorithms
                  for Permutation Flowshop Problems},
  journal      = ejor,
  year         = 2019,
  volume       = 276,
  pages        = {409--421},
  issue        = 2,
  doi          = {10.1016/j.ejor.2019.01.018},
  keywords     = {EMILI},
  abstract     = {Stochastic local search methods are at the core of many
                  effective heuristics for tackling different permutation
                  flowshop problems (PFSPs). Usually, such algorithms require a
                  careful, manual algorithm engineering effort to reach high
                  performance. An alternative to the manual algorithm
                  engineering is the automated design of effective SLS
                  algorithms through building flexible algorithm frameworks and
                  using automatic algorithm configuration techniques to
                  instantiate high-performing algorithms. In this paper, we
                  automatically generate new high-performing algorithms for
                  some of the most widely studied variants of the PFSP. More in
                  detail, we (i) developed a new algorithm framework, EMILI,
                  that implements algorithm-specific and problem-specific
                  building blocks; (ii) define the rules of how to compose
                  algorithms from the building blocks; and (iii) employ an
                  automatic algorithm configuration tool to search for high
                  performing algorithm configurations. With these ingredients,
                  we automatically generate algorithms for the PFSP with the
                  objectives makespan, total completion time and total
                  tardiness, which outperform the best algorithms obtained by a
                  manual algorithm engineering process.}
}

@Article{PagStu2020:itor,
  author =       Pagnozzi #and# Stuetzle,
  title =        {Evaluating the impact of grammar complexity in automatic algorithm design},
  journal =      itor,
  pages =        {1--26},
  doi =          {10.1111/itor.12902},
  year =         2020
}

@Article{PagStu2021:orp,
  author       = Pagnozzi #and# Stuetzle,
  title        = {Automatic design of hybrid stochastic local search algorithms
                  for permutation flowshop problems with additional
                  constraints},
  journal      = orp,
  year         = 2021,
  volume       = 8,
  pages        = 100180,
  doi          = {10.1016/j.orp.2021.100180},
  abstract     = {Automatic design of stochastic local search algorithms has
                  been shown to be very effective in generating algorithms for
                  the permutation flowshop problem for the most studied
                  objectives including makespan, flowtime and total
                  tardiness. The automatic design system uses a configuration
                  tool to combine algorithmic components following a set of
                  rules defined as a context-free grammar. In this paper we use
                  the same system to tackle two of the most studied additional
                  constraints for these objectives: sequence dependent setup
                  times and no-idle constraint. Additional components have been
                  added to adapt the system to the new problems while keeping
                  intact the grammar structure and the experimental setup. The
                  experiments show that the generated algorithms outperform the
                  state of the art in each case.}
}

@Article{PajBlaHerMar2021archiving,
  title =        {A Comparison of Archiving Strategies for Characterization of
                  Nearly Optimal Solutions under Multi-Objective Optimization},
  author =       {Pajares, Alberto and Blasco, Xavier and Herrero, Juan Manuel
                  and Mart{\'i}nez, Miguel A.},
  journal =      mathematics,
  year =         2021,
  doi =          "10.3390/math9090999",
  volume =       9,
  number =       9,
  pages =        "999",
  abstract =     "In a multi-objective optimization problem, in addition to
                  optimal solutions, multimodal and/or nearly optimal
                  alternatives can also provide additional useful information
                  for the decision maker. However, obtaining all nearly optimal
                  solutions entails an excessive number of
                  alternatives. Therefore, to consider the nearly optimal
                  solutions, it is convenient to obtain a reduced set, putting
                  the focus on the potentially useful alternatives. These
                  solutions are the alternatives that are close to the optimal
                  solutions in objective space, but which differ significantly
                  in the decision space. To characterize this set, it is
                  essential to simultaneously analyze the decision and
                  objective spaces. One of the crucial points in an
                  evolutionary multi-objective optimization algorithm is the
                  archiving strategy. This is in charge of keeping the solution
                  set, called the archive, updated during the optimization
                  process. The motivation of this work is to analyze the three
                  existing archiving strategies proposed in the literature
                  (ArchiveUpdate$P_{Q,\epsilon}D_{xy}$, Archive\_nevMOGA, and
                  targetSelect) that aim to characterize the potentially useful
                  solutions. The archivers are evaluated on two benchmarks and
                  in a real engineering example. The contribution clearly shows
                  the main differences between the three archivers. This
                  analysis is useful for the design of evolutionary algorithms
                  that consider nearly optimal solutions.",
  keywords =     "multi-objective optimization; nearly optimal solutions;
                  non-epsilon dominance; multimodality; decision space
                  diversity; archiving strategy; evolutionary algorithm;
                  non-linear parametric identification",
}

@Article{PalGooSor2014,
  author =       {Daniel {Palhazi Cuervo}} #and# "Peter Goos" #and# Sorensen #and# {Emely Arr{\'{a}}iz},
  title =        {An Iterated Local Search Algorithm for the Vehicle Routing Problem
               with Backhauls},
  journal =      ejor,
  year =         2014,
  volume =       237,
  number =       2,
  pages =        {454--464}
}

@Article{Palubeckis2006,
  title =        {Iterated tabu search for the unconstrained binary quadratic
                  optimization problem},
  author =       {Palubeckis, Gintaras},
  journal =      inf,
  volume =       17,
  number =       2,
  pages =        {279--296},
  year =         2006,
  publisher =    {Institute of Mathematics and Informatics},
  doi =          {10.15388/Informatica.2006.138}
}

@Article{PanRui2012ejor,
  title =        {Local Search Methods for the Flowshop Scheduling Problem with Flowtime Minimization},
  author =       "Pan, Quan-Ke" #and# Ruiz_Ruben,
  journal =      ejor,
  volume =       222,
  number =       1,
  pages =        "31--43",
  year =         2012
}

@Article{PanRui2013cor,
  title =        {A Comprehensive Review and Evaluation of Permutation
                  Flowshop Heuristics to Minimize Flowtime},
  author =       "Pan, Quan-Ke" #and# Ruiz_Ruben,
  journal =      cor,
  volume =       40,
  number =       1,
  pages =        "117--128",
  year =         2013,
  alias =        "PanRui2012"
}

@Article{PanRuiAlf2017:cor,
  author = 	 Pan_QK #and# Ruiz_Ruben #and# Alfaro-Fernandez,
  title = 	 {Iterated Search Methods for Earliness and Tardiness Minimization in Hybrid Flowshops with Due Windows},
  journal = 	 cor,
  year = 	 2017,
  volume = 	 80,
  pages = 	 {50--60}
}

@Article{PanTasLia2008,
  title =        "A Discrete Differential Evolution Algorithm for the
                  Permutation Flowshop Scheduling Problem ",
  journal =      cie,
  volume =       55,
  number =       4,
  pages =        "795 -- 816",
  year =         2008,
  author =       "Quan-Ke Pan and Mehmet Fatih Tasgetiren and Yun-Chia
                  Liang",
}

@Article{PanWanZha2008,
  year =         2008,
  journal =      ijamt,
  volume =       38,
  number =       {7-8},
  title =        {An improved iterated greedy algorithm for the
                  no-wait flow shop scheduling problem with makespan
                  criterion},
  author =       {Pan, Quan-Ke and Wang, Ling and Zhao, Bao-Hua},
  pages =        {778--786}
}

@Article{PanYang2009,
  title =        {A survey on transfer learning},
  author =       {Pan, Sinno Jialin and Yang, Qiang},
  journal =      tkde,
  volume =       22,
  number =       10,
  pages =        {1345--1359},
  year =         2009
}

@Article{PaqSchStu07:aor,
  author =       Paquete #and# Schiavinotto #and# Stuetzle,
  title =        "On Local Optima in Multiobjective Combinatorial
                  Optimization Problems",
  journal =      aor,
  year =         2007,
  volume =       156,
  pages =        {83--97},
  doi =          {10.1007/s10479-007-0230-0},
  keywords =     {Pareto local search, PLS},
  abstract =     {In this article, local optimality in multiobjective
                  combinatorial optimization is used as a baseline for
                  the design and analysis of two iterative improvement
                  algorithms. Both algorithms search in a neighborhood
                  that is defined on a collection of sets of feasible
                  solutions and their acceptance criterion is based on
                  outperformance relations. Proofs of the soundness
                  and completeness of these algorithms are given.}
}

@Article{PaqStu06:mqap,
  author =       Paquete #and# Stuetzle,
  title =        "A study of stochastic local search algorithms for
                  the biobjective {QAP} with correlated flow matrices",
  journal =      ejor,
  year =         2006,
  volume =       169,
  number =       3,
  alias = {lpaquete:14},
  pages =        {943--959},
}

@Article{PaqStu09:cor,
  author =       Paquete #and# Stuetzle,
  title =        {Design and analysis of stochastic local search for
                  the multiobjective traveling salesman problem},
  journal =      cor,
  year =         2009,
  volume =       36,
  number =       9,
  pages =        {2619--2631},
  doi =          {10.1016/j.cor.2008.11.013}
}

@Article{ParDoeHar2009,
  author =       {Parragh, S. N.} #and# Doerner #and# Hartl #and# Gandibleux,
  title =        {A heuristic two-phase solution approach for the
                  multi-objective dial-a-ride problem},
  journal =      networks,
  volume =       54,
  number =       4,
  pages =        {227--242},
  year =         2009,
}

@Article{ParJoh1997,
  author  = {Rebecca Parsons and Mark Johnson},
  title       = {A Case Study in Experimental Design Applied to Genetic Algorithms with Applications to {DNA} Sequence Assembly},
  journal  = {American Journal of Mathematical and Management Sciences},
  publisher = taylorfrancis,
  volume = 17,
  number = {3-4},
  pages    = {369--396},
  year       = 1997,
  doi         = {10.1080/01966324.1997.10737444}
}

@Article{ParKim98,
  author   = {Moon-Won Park and Yeong-Dae Kim},
  title    = {A systematic procedure for setting parameters in simulated annealing algorithms},
  journal  = cor,
  volume   = 25,
  number   = 3,
  pages    = {207--217},
  year     = 1998,
  doi      = {10.1016/S0305-0548(97)00054-3}
}

@Article{ParLopFre2002:ieee-tec,
  author =   {R. S. Parpinelli and H. S. Lopes and A. A. Freitas},
  title =    {Data Mining with an Ant Colony Optimization Algorithm},
  journal =  tec,
  year =     2002,
  volume =   6,
  number =   4,
  pages =    {321--332}
}

@Article{ParQuo1995antlr,
  author       = {Parr, Terence J. and Quong, Russell W.},
  title        = {{ANTLR}: A predicated-{LL} (k) parser generator},
  journal      = spe,
  year         = 1995,
  volume       = 25,
  number       = 7,
  pages        = {789--810}
}

@Article{ParVas2007:ejor,
        author =      Parreiras #and# Vascocelos,
        title =       "A multiplicative version of {PROMETHEE II} applied to
                   multiobjective optimization problems",
        journal =     ejor,
        year =        2007,
        volume =      183,
        pages =       {729--740}
}

@Article{Paul2010,
  title =        {Comparative performance of tabu search and simulated annealing heuristics for the quadratic assignment problem},
  author =       Paul_G,
  journal =      orl,
  volume =       38,
  number =       6,
  pages =        {577--581},
  year =         2010,
  publisher =    elsevier
}

@Article{Pearl2019:cacm,
  title =        {The seven tools of causal inference, with reflections on machine learning},
  author =       Pearl_J,
  journal =      cacm,
  volume =       62,
  number =       3,
  pages =        {54--60},
  year =         2019
}

@Article{PedNesCan2011:asc,
  author    = {Mart{\'i}n Pedemonte and Sergio Nesmachnow and H{\'e}ctor Cancela},
  title     = {A survey on parallel ant colony optimization},
  journal   = asoco,
  volume    = 11,
  number    = 8,
  pages     = {5181--5197},
  year      = 2011,
}

@Article{PelBirStu2012:si,
  author =       Pellegrini #and# Birattari #and# Stuetzle,
  title =        {A Critical Analysis of Parameter Adaptation in Ant Colony Optimization},
  journal =      swarm,
  year =         2012,
  volume =       6,
  number =       1,
  pages =        {23--48},
  doi = {10.1007/s11721-011-0061-0}
}

@Article{PelCasPes2012its,
  author =       Pellegrini #and# "L. Castelli" #and# "R. Pesenti",
  title =        {Metaheuristic algorithms for the simultaneous slot
                  allocation problem},
  journal =      iet-its,
  year =         2012,
  volume =       6,
  number =       4,
  pages =        {453--462},
  month =        dec,
  doi =          {10.1049/iet-its.2011.0179},
}

@Article{PelMasBirStu14,
  author =       Pellegrini #and# Mascia_F #and# Stuetzle #and#
                  Birattari,
  title =        {On the Sensitivity of Reactive Tabu Search to its
                  Meta-parameters},
  journal =      soco,
  year =         2014,
  volume =       18,
  number =       11,
  pages =        {2177--2190},
  doi =          {10.1007/s00500-013-1192-6}
}

@Article{PenSubOch2013:joh,
  author =       {Puca Huachi {Vaz Penna}} #and# Subramanian #and# Ochi,
  title =        {An Iterated Local Search Heuristic for the Heterogeneous Fleet Vehicle Routing Problem},
  journal =      joh,
  year =         2013,
  volume =       19,
  number =       2,
  pages =        {201--232}}

@Article{Per2020challenge,
  title =        "Challenge to scientists: does your ten-year-old code still
                  run?",
  author =       "Jeffrey M. Perkel",
  journal =      nature,
  volume =       584,
  pages =        "556--658",
  year =         2020,
  doi =          "10.1038/d41586-020-02462-7",
  keywords =     "reproducibility; software engineering; ReScience C; Ten Years
                  Reproducibility Challenge; code reusability",
}

@Article{PerLopStu2015si,
  author =       Perez_L #and# Lopez-Ibanez #and# Stuetzle,
  title =        {Ant colony optimization on a limited budget of evaluations},
  journal =      swarm,
  year =         2015,
  doi =          {10.1007/s11721-015-0106-x},
  supplement =   {http://iridia.ulb.ac.be/supp/IridiaSupp2015-004},
  pdf =          "PerLopStu2015si.pdf",
  volume =       9,
  number =       {2-3},
  pages =        {103--124},
}

@Article{PerRuiNes2018,
  author =       {P{\'{e}}res, Matias and Ruiz, Germ{\'{a}}n and Nesmachnow,
                  Sergio and Olivera, Ana C.},
  journal =      asoco,
  keywords =     {Multiobjective evolutionary
                  algorithms,Pollution,Simulation,Traffic flow},
  title =        {Multiobjective evolutionary optimization of traffic flow and
                  pollution in {Montevideo}, {Uruguay}},
  year =         2018,
  alias =        "Peres2018",
  volume =       70,
  pages =        {472--485},
  publisher =    {Elsevier}
}

@Article{PesUchAraRod2010,
  author = 	 {A. Pessoa and E. Uchoa and M. Arag{\~a}o and R. Rodrigues},
  title = 	 {Exact Algorithm over an Arc-time-indexed formulation for Parallel Machine Scheduling Problems},
  journal = 	 mpc,
  year = 	 2010,
  volume = 	 2,
  number = 	 {3--4},
  pages = 	 {259--290}
}

@Article{Pesant98tsptw,
  author =       Pesant #and# Gendreau #and# Potvin #and# Rousseau_JM,
  title =        {An Exact Constraint Logic Programming Algorithm
  for the Traveling Salesman Problem with Time Windows},
  journal =      ts,
  year =         1998,
  volume =       32,
  pages =        {12--29},
  pdf = {PesGenPot96-Exact constraint logic programming algorithm for the TSPTW.ps}
}

@Article{Pet1998boeing,
  author =       {Charles W. Petit},
  title =        {Touched by nature: putting evolution to work on the assembly
                  line},
  journal =      {U.S. News \& World Report},
  volume =       125,
  number =       4,
  month =        jul,
  year =         1998,
  pages =        {43--45},
  url =
                  {http://dynamics.org/~altenber/IN_THE_MEDIA/US_NEWS.7-27-98/index.html},
  annote =       "Evolutionary optimization of turbine design of the
                  Boeing~777~GE"
}

@Article{PetBonGre2014gtoc5,
  author       = {Petropoulos, Anastassios E. and Bonfiglio, Eugene P. and
                  Grebow, Daniel J. and Lam, Try and Parker, Jeffrey S. and
                  Arrieta, Juan and Landau, Damon F. and Anderson, Rodney
                  L. and Gustafson, Eric D. and Whiffen, Gregory J. and
                  Finlayson, Paul A. and Sims, Jon A.},
  title        = {{GTOC5}: Results from Jet Propulsion Lab},
  journal      = actafutura,
  year         = 2014,
  volume       = 8,
  pages        = {21--27},
  doi          = {10.2420/AF08.2014.21},
  abstract     = {We present the methods and results of the Jet Propulsion
                  Laboratory team in the 5th Global Trajectory Optimization
                  Competition. Our broad-search strategy utilized several
                  recently developed phase-free metrics for rapidly narrowing
                  the search options. Two different, adaptive, branch-and-prune
                  strategies were employed to build up asteroid sequences using
                  a rendezvous-flyby-rendezvous building block, with a robust
                  local optimizer in the loop. The best of these sequences were
                  refined end-to-end using the same direct optimizer, to yield
                  the winning 18-point, 18-asteroid solution.}
}

@Article{PetHarHarLan2018gi,
  author =       {Petke, Justyna and Haraldsson, Saemundur O. and Harman, Mark}
                  #and# Langdon_WB #and# {White, David R.} #and# Woodward_J,
  journal =      tec,
  title =        {Genetic Improvement of Software: A Comprehensive Survey},
  year =         2018,
  volume =       22,
  number =       3,
  pages =        {415--432},
  doi =          {10.1109/TEVC.2017.2693219}
}

@Article{PetZil2006parallel,
  author =       {Petrik, Marek} #and# Zilberstein,
  journal =      amai,
  number =       1,
  pages =        {85--106},
  title =        {Learning parallel portfolios of algorithms},
  volume =       48,
  year =         2006,
  keywords =     "algorithm selection"
}

@Article{Pezeshk96,
  author =       Pezeshk #and# Helweg,
  title =        {Adaptative Search Optimisation in reducing pump operation
                  costs},
  journal =      jwrpm,
  volume =       122,
  number =       1,
  pages =        {57--63},
  date =         "1996-01/1996-02",
  year =         1996,
  month =        jan # " / " # feb,
}

@Article{PheKok2003interactive,
  title =        {An interactive evolutionary metaheuristic for multiobjective
                  combinatorial optimization},
  author =       {Phelps, Selcen} #and# Koksalan,
  journal =      ms,
  volume =       49,
  number =       12,
  pages =        {1726--1738},
  year =         2003,
}

@Article{PieKhuSav2007investigation,
  title =        {An investigation on preference order ranking scheme for
                  multiobjective evolutionary optimization},
  author =       {di Pierro, Francesco and Khu, Soon-Thiam} #and# Savic,
  journal =      tec,
  volume =       11,
  number =       1,
  pages =        {17--45},
  year =         2007,
}

@Article{PinVinSin2020improving,
  title =        {Improving Reproducibility in Machine Learning Research (A
                  Report from the {NeurIPS} 2019 Reproducibility Program)},
  author =       {Joelle Pineau and Philippe Vincent-Lamarre and Koustuv Sinha
                  and Vincent Larivière and Alina Beygelzimer and Florence
                  d'Alché-Buc and Emily Fox and Hugo Larochelle},
  year =         2020,
  journal =      arxiv # {2003.12206 [cs.LG]},
  url =          {https://arxiv.org/abs/2003.12206}
}

@Article{Pis2005where,
  author =       Pisinger,
  journal =      cor,
  number =       9,
  pages =        {2271--2284},
  title =        {Where are the hard knapsack problems?},
  volume =       32,
  year =         2005
}

@Article{PisRop2007:cor,
  author =       Pisinger #and# Ropke,
  title =        {A General Heuristic for Vehicle Routing Problems},
  journal =      cor,
  year =         2007,
  volume =       34,
  number =       8,
  pages =        {2403--2435}
}

@Article{PitAlmDoe06,
  author =       "Rapeepan Pitakaso" #and# Almeder #and# Doerner
                  #and# Hartl,
  title =        {Combining exact and population-based methods for the
                  Constrained Multilevel Lot Sizing Problem},
  journal =      ijpr,
  year =         2006,
  volume =       44,
  number =       22,
  pages =        {4755--4771},
}

@Article{PitAlmDoe07,
  author =       "Rapeepan Pitakaso and " # Almeder #and# Doerner
                  #and# Hartl,
  title =        {A {\MaxMinAntSystem} for unconstrained multi-level
                  lot-sizing problems},
  volume =       34,
  number =       9,
  journal =      cor,
  year =         2007,
  keywords =     {Ant colony optimization, Material requirements
                  planning, Multi-level lot-sizing, {Wagner-Whitin}
                  algorithm},
  pages =        {2533--2552},
  doi =          {10.1016/j.cor.2005.09.022},
  abstract =     { In this paper, we present an ant-based algorithm
                  for solving unconstrained multi-level lot-sizing
                  problems called ant system for multi-level
                  lot-sizing algorithm {(ASMLLS).} We apply a hybrid
                  approach where we use ant colony optimization in
                  order to find a good lot-sizing sequence, i.e. a
                  sequence of the different items in the product
                  structure in which we apply a modified
                  {Wagner-Whitin} algorithm for each item
                  separately. Based on the setup costs each ant
                  generates a sequence of items. Afterwards a simple
                  single-stage lot-sizing rule is applied with
                  modified setup costs. This modification of the setup
                  costs depends on the position of the item in the
                  lot-sizing sequence, on the items which have been
                  lot-sized before, and on two further parameters,
                  which are tried to be improved by a systematic
                  search. For small-sized problems {ASMLLS} is among
                  the best algorithms, but for most medium- and
                  large-sized problems it outperforms all other
                  approaches regarding solution quality as well as
                  computational time.},
}

@Article{Ples2018repro,
  doi =          {10.3389/fninf.2017.00076},
  year =         2018,
  month =        jan,
  publisher =    {Frontiers Media {SA}},
  volume =       11,
  author =       {Hans E. Plesser},
  title =        {Reproducibility vs.\ Replicability: A Brief History of a
                  Confused Terminology},
  journal =      {Frontiers in Neuroinformatics}
}

@Article{PorGonAllHsu2017:ejor,
  author = 	 {Daniel Porumbel and Gilles Goncalves and Hamid Allaoui and Tient{\'e} Hsu},
  title = 	 {Iterated Local Search and Column Generation to solve Arc-Routing as a Permutation Set-Covering Problem},
  journal = 	 ejor,
  year = 	 2017,
  volume = 	 256,
  number =       2,
  pages = 	 {349--367}
}

@Article{PorParDoa++2013,
  author =       {Juan Porta and Jorge Parapar and Ram{\'o}n Doallo and Vasco Barbosa and In{\'e}s Sant{\'e} and Rafael Crecente and Carlos D{\'i}az},
  title =        {A Population-based Iterated Greedy Algorithm for the Delimitation and Zoning of Rural Settlements},
  journal =      {Computers, Environment and Urban Systems},
  year =         2013,
  volume =       39,
  pages =        {12--26}
}

@Article{PotBen96vrptw_gene,
  author =       Potvin #and# Bengio_S,
  title =        "The Vehicle Routing Problem with Time Windows Part
                  {II}: Genetic Search",
  journal =      informsjoc,
  year =         1996,
  volume =       8,
  pages =        {165--172},
}

@Article{Pra2009jwrpm,
  author =       Prasad,
  title =        "Design of pumped water distribution networks with
                  storage",
  journal =      jwrpm,
  year =         2009,
  volume =       136,
  number =       4,
  pages =        {129--136},
}

@Article{PraPac2015:joh,
  author =       {Marco Pranzo and D. Pacciarelli},
  title =        {An Iterated Greedy Metaheuristic for the Blocking Job Shop Scheduling Problem},
  journal =      joh,
  year =         2016,
  volume =    22,
  number =    4,
  pages =     {587--611},
  doi =      {10.1007/s10732-014-9279-5},
}

@Article{PraAveLem2019learning,
  author       = {Prates, Marcelo and Avelar, Pedro H. C. and Lemos, Henrique
                  and Lamb, Luis C. and Vardi, Moshe Y.},
  title        = {Learning to Solve {NP-Complete} Problems: A Graph Neural
                  Network for Decision {TSP}},
  journal      = aaai,
  year         = 2019,
  volume       = 33,
  number       = 01,
  pages        = {4731--4738},
  month        = jul,
  issn         = {2159-5399},
  doi          = {10.1609/aaai.v33i01.33014731},
  publisher    = {Association for the Advancement of Artificial Intelligence
                  (AAAI)}
}

@Article{PriKumSug2023trial,
  author       = {Kenneth V. Price and Abhishek Kumar} #and# Suganthan,
  title        = {Trial-based dominance for comparing both the speed and
                  accuracy of stochastic optimizers with standard
                  non-parametric tests},
  journal      = swevo,
  year         = 2023,
  volume       = 78,
  pages        = 101287,
  doi          = {10.1016/j.swevo.2023.101287},
  keywords     = {Benchmarking, Two-variable non-parametric tests, Evolutionary
                  algorithms, Dominance, Stochastic optimization, Numerical
                  optimization, Mann-Whitney test}
}

@Article{Prim1957,
  title =        {Shortest connection networks and some generalizations},
  author =       {Prim, Robert Clay},
  journal =      bstj,
  volume =       36,
  number =       6,
  pages =        {1389--1401},
  year =         1957
}

@Article{ProBisBou2018tuna,
  title =        {Tunability: Importance of Hyperparameters of Machine Learning
                  Algorithms},
  author =       "Probst, Philipp" #and# Bischl_B #and# "Boulesteix,
                  Anne-Laure",
  journal =      arxiv # {1802.09596},
  year =         2018,
  url =        {https://arxiv.org/abs/1802.09596},
  keywords =     {parameter importance},
}

@Article{ProBisBou2019tuna,
  author =       "Probst, Philipp" #and# Bischl_B #and# "Boulesteix,
                  Anne-Laure",
  title =        {Tunability: Importance of Hyperparameters of Machine Learning
                  Algorithms},
  journal =      jmlr,
  year =         2019,
  volume =       20,
  number =       53,
  pages =        {1--32}
}

@Article{ProMue2012,
  title =        {Design of computer experiments: space filling and beyond},
  author =       {Pronzato, Luc and M\"uller, Werner G.},
  journal =      statscomp,
  year =         2012,
  number =       3,
  pages =        {681--701},
  volume =       22,
  keywords =     {Kriging; Entropy; Design of experiments; Space-filling;
                  Sphere packing; Maximin design; Minimax design},
  alias =        "Pronzato2012"
}

@Article{Psaraftis1995:aor,
  author =       {Harilaos N. Psaraftis},
  title =        {Dynamic Vehicle Routing: Status and Prospects},
  journal =      aor,
  year =         1995,
  volume =       61,
  pages =        {143--164}}

@Article{PukHei2006,
  title =        {Optimizing heuristic search in forest planning},
  author =       {Pukkala, Timo and Heinonen, Tero},
  journal =      nlarwa,
  volume =       7,
  number =       5,
  year =         2006,
  pages =        {1284--1297},
  publisher =    elsevier
}

@Article{PulTac2009aqme,
  title =        {A self-adaptive multi-engine solver for quantified {Boolean}
                  formulas},
  author =       {Pulina, Luca and Tacchella, Armando},
  journal =      constr,
  volume =       14,
  number =       1,
  pages =        {80--116},
  year =         2009,
}

@Article{PurFle2007tec,
  title =        {On the Evolutionary Optimization of Many Conflicting
                  Objectives},
  author =       Purshouse_R #and# Fleming,
  journal =      tec,
  year =         2007,
  doi =          {10.1109/TEVC.2007.910138},
  volume =       11,
  number =       6,
  pages =        {770--784}
}

@Article{QiMaLiu2014moead,
  title =        {{MOEA/D} with adaptive weight adjustment},
  author =       {Qi, Yutao and Ma, Xiaoliang and Liu, Fang and Jiao, Licheng
                  and Sun, Jianyong and Wu, Jianshe},
  journal =      ec,
  year =         2014,
  number =       2,
  pages =        {231--264},
  volume =       22,
  publisher =    mit-pub,
  annote =       "Uses an external population",
  doi =          "10.1162/EVCO_a_00109"
}

@Article{QuiReeKel2017direct,
  title =        {Direct policy search for robust multi-objective management of
                  deeply uncertain socio-ecological tipping points},
  author =       {Quinn, Julianne D.} #and# Reed_P #and# {Keller, Klaus},
  journal =      ems,
  volume =       92,
  pages =        {125--141},
  year =         2017,
  publisher =    {Elsevier}
}

@Article{RadRuiBor2009,
  title                    = {New High Performing Heuristics for Minimizing Makespan in Permutation Flowshops},
  author                   = Rad_SF #and# Ruiz_Ruben #and# Boroojerdian,
  journal                  = omega,
  year                     = 2009,
  number                   = 2,
  pages                    = {331--345},
  volume                   = 37
}

@Article{Raj93,
  title =        "Heuristic algorithm for scheduling in a flowshop to
                  minimize total flowtime",
  author =       Rajendran,
  journal =      ijpe,
  volume =       29,
  number =       1,
  pages =        {65--73},
  year =         1993,
}

@Article{RajZie04,
  author =       Rajendran #and# Ziegler,
  title =        "Ant-colony algorithms for permutation flowshop
                  scheduling to minimize makespan/total flowtime of
                  jobs",
  journal =      ejor,
  volume =       155,
  number =       2,
  pages =        "426--438",
  year =         2004,
}

@Article{RajZie1997,
  author =       Rajendran #and# Ziegler,
  title =        {An efficient heuristic for scheduling in a flowshop to
                  minimize total weighted flowtime of jobs},
  journal =      ejor,
  volume =       103,
  number =       1,
  pages =        {129--138},
  year =         1997,
  issn =         {0377 -- 2217},
  doi =          "10.1016/S0377-2217(96)00273-1",
}

@Article{RamBir2020appsci,
	title     = {Automatic Design of Collective Behaviors for Robots that Can Display and Perceive Colors},
	author    = GarzonRamos_D #and# Birattari,
	journal   = appsci,
	volume    = 10,
	number    = 13,
	pages     = {4654},
	year      = 2020,
	publisher = {Multidisciplinary Digital Publishing Institute}
}

@Article{RamMirSer2021foodplan,
  author =       {Ramos-Pérez, Juan-Manuel and Miranda, Gara and Segredo,
                  Eduardo and León, Coromoto and Rodríguez-León, Casiano},
  title =        {Application of Multi-Objective Evolutionary Algorithms for
                  Planning Healthy and Balanced School Lunches},
  journal =      mathematics,
  month =        dec,
  publisher =    mdpi,
  volume =       9,
  year =         2021,
  number =       1,
  pages =        80,
  doi =          {10.3390/math9010080},
  abstract =     {A multi-objective formulation of the Menu Planning Problem,
                  which is termed the Multi-objective Menu Planning Problem, is
                  presented herein. Menu planning is of great interest in the
                  health field due to the importance of proper nutrition in
                  today's society, and particularly, in school
                  canteens. In addition to considering the cost of the meal
                  plan as the classic objective to be minimized, we also
                  introduce a second objective aimed at minimizing the degree
                  of repetition of courses and food groups that a particular
                  meal plan consists of. The motivation behind this particular
                  multi-objective formulation is to offer a meal plan that is
                  not only affordable but also varied and balanced from a
                  nutritional standpoint. The plan is designed for a given
                  number of days and ensures that the specific nutritional
                  requirements of school-age children are satisfied. The main
                  goal of the current work is to demonstrate the
                  multi-objective nature of the said formulation, through a
                  comprehensive experimental assessment carried out over a set
                  of multi-objective evolutionary algorithms applied to
                  different instances. At the same time, we are also interested
                  in validating the multi-objective formulation by performing
                  quantitative and qualitative analyses of the solutions
                  attained when solving it. Computational results show the
                  multi-objective nature of the said formulation, as well as
                  that it allows suitable meal plans to be obtained.}
}

@Article{RamMonMor2011jors,
  title =        {Extending the use of scenario planning and {MCDA} for the
                  evaluation of strategic options},
  author =       {Ram, Camelia and Montibeller, Gilberto and Morton, Alec},
  journal =      jors,
  volume =       62,
  number =       5,
  pages =        {817--829},
  year =         2011,
}

@Article{RaoSal2007hydroinf,
  author =       Rao_Zhengfu #and# Salomons,
  title =        {Development of a real-time, near-optimal control
                  process for water-distribution networks},
  journal =      hydroinf,
  year =         2007,
  volume =       9,
  number =       1,
  doi =          {10.2166/hydro.2006.015},
  pages =        {25--37},
}

@Article{RarUzs2001:joh,
  author = 	 {Ronald L. Rardin and Reha Uzsoy},
  title = 	 {Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial},
  journal = 	 joh,
  year = 	 2001,
  volume = 	 7,
  number = 	 3,
  pages = 	 {261--304}
}

@Article{RasMusKar2019vrp,
  author =       {Jussi Rasku} #and# Musliu_N #and# {Tommi K{\"a}rkk{\"a}inen},
  title =        {On automatic algorithm configuration of vehicle routing
                  problem solvers},
  journal =      jvra,
  year =         2019,
  month =        feb,
  volume =       2,
  number =       {1-4},
  pages =        {1--22},
  doi =          "10.1007/s41604-019-00010-9",
  keywords =     "irace, SMAC, GGA, REVAC, VRP"
}

@Article{Rech2000case,
  title =        {Case studies in evolutionary experimentation and computation},
  author =       Rechenberg,
  journal =      CMinAppMechEng,
  volume =       186,
  number =       {2-4},
  pages =        {125--140},
  year =         2000,
  doi =          "10.1016/S0045-7825(99)00381-3",
  publisher =    {Elsevier}
}

@Article{ReeEre2004jors,
  author =       Reeves_CR #and# {Eremeev, A. V.},
  title =        {Statistical analysis of local search landscapes},
  journal =      jors,
  pages =        {687--693},
  volume =       55,
  number =       7,
  year =         2004,
  epub =         {http://www.jstor.org/stable/4102015}
}

@Article{ReeGon1989imcdm,
  title =        {A comparison of two interactive {MCDM} procedures},
  author =       {Reeves, Gary R. and Gonzalez, Juan J.},
  journal =      ejor,
  volume =       41,
  number =       2,
  pages =        {203--209},
  year =         1989,
  doi =          {10.1016/0377-2217(89)90385-8},
  publisher =    {Elsevier},
  keywords =     "artificial DM, interactive"
}

@Article{ReeHadHerKas2013water,
  title =        {Evolutionary multiobjective optimization in water resources:
                  The past, present, and future},
  author =       Reed_P #and# Hadka_D #and# {Herman, Jonathan D.} #and#
                  Kasprzyk #and# Kollat,
  journal =      awr,
  volume =       51,
  pages =        {438--456},
  year =         2013,
}

@Article{CheLiYao2019,
  title =        {Standing on the shoulders of giants: Seeding search-based
                  multi-objective optimization with prior knowledge for
                  software service composition},
  author =       {Chen, Tao} #and# Li_Miqing #and# Yao_Xin,
  journal =      infsoftech,
  year =         2019,
  pages =        {155--175},
  volume =       114,
  publisher =    {Elsevier},
  annote =       "Example of deteroriation in archiving"
}

@Article{RehZaeFisRud2022bench,
  author       = {Rehbach, Frederik} #and# Zaefferer #and# Fischbach #and#
                  Rudolph_G #and# Bartz-Beielstein,
  title        = {Benchmark-Driven Configuration of a Parallel Model-Based
                  Optimization Algorithm},
  journal      = tec,
  year         = 2022,
  volume       = 26,
  number       = 6,
  pages        = {1365--1379},
  doi          = {10.1109/TEVC.2022.3163843}
}

@Article{Rei91a,
  author =       Reinelt,
  title =        {{TSPLIB} --- A Traveling Salesman Problem Library},
  journal =      orsa,
  year =         1991,
  volume =       3,
  number =       4,
  pages =        {376--384},
}

@Article{ReiDoeHar04:cor,
  author =       Reimann #and# Doerner #and# Hartl,
  title =        "{D}-ants: {Savings} based ants divide and conquer
                  the vehicle routing problems",
  journal =      cor,
  year =         2004,
  volume =       31,
  number =       4,
  pages =        {563--591}
}

@Article{ReiLau06,
  author =       Reimann #and# Laumanns,
  title =        {Savings based ant colony optimization for the
                  capacitated minimum spanning tree problem},
  volume =       33,
  number =       6,
  journal =      cor,
  year =         2006,
  keywords =     {Ant colony Optimization, Capacitated minimum
                  spanning tree problem},
  pages =        {1794--1822},
  doi =          {10.1016/j.cor.2004.11.019},
  abstract =     { The problem of connecting a set of client nodes
                  with known demands to a root node through a minimum
                  cost tree network, subject to capacity constraints
                  on all links is known as the capacitated minimum
                  spanning tree {(CMST)} problem. As the problem is
                  {NP-hard,} we propose a hybrid ant colony
                  optimization {(ACO)} algorithm to tackle it
                  heuristically. The algorithm exploits two important
                  problem characteristics: (i) the {CMST} problem is
                  closely related to the capacitated vehicle routing
                  problem {(CVRP),} and (ii) given a clustering of
                  client nodes that satisfies capacity constraints,
                  the solution is to find a {MST} for each cluster,
                  which can be done exactly in polynomial time. Our
                  {ACO} exploits these two characteristics of the
                  {CMST} by a solution construction originally
                  developed for the {CVRP.} Given the {CVRP} solution,
                  we then apply an implementation of Prim's algorithm
                  to each cluster to obtain a feasible {CMST}
                  solution. Results from a comprehensive computational
                  study indicate the efficiency and effectiveness of
                  the proposed approach.},
}

@Article{RenFenKeZha2010:cie,
  author = 	 {Zhi-Gang Ren and Zu-Ren Feng and Liang-Jun Ke and Zhao-Jun Zhang},
  title = 	 {New Ideas for Applying Ant Colony Optimization to the Set Covering Problem},
  journal = 	 cie,
  year = 	 2010,
  volume = 	 58,
  number = 	 4,
  pages = 	 {774--784}
}

@Article{ReyCoe06,
  author =       {M. Reyes-Sierra } #and# Coello,
  title =        "Multi-objective particle swarm optimizers: A survey of the
                  state-of-the-art",
  year =         2006,
  journal =      ijcir,
  volume =       2,
  number =       3,
  pages =        {287--308}
}

@Article{Reynolds1987,
  author =       "Craig W. Reynolds",
  title =        "Flocks, Herds, and Schools: A Distributed Behavioral Model",
  journal =      acm-cg,
  volume =       21,
  number =       4,
  pages =        "25--34",
  year =         1987,
  alias =        "Reynolds1987",
}

@Article{RezAraMeh2022anchoring,
  title =        {Analyzing anchoring bias in attribute weight elicitation of
                  {SMART}, {Swing}, and best-worst method},
  doi =          {10.1111/itor.13171},
  journal =      itor,
  author =       {Rezaei, Jafar and Arab, Alireza and Mehregan, Mohammadreza},
  year =         2022,
  keywords =     {anchoring bias, best-worst method, cognitive bias, MADM,
                  multi-attribute weighting, SMART, Swing}
}

@Article{RezHej2005,
  title                    = {Flowshop-scheduling Problems with Makespan Criterion: A Review},
  author                   = {S. Reza Hejazi and S. Saghafian},
  journal                  = ijpr,
  year                     = 2005,
  number                   = 14,
  pages                    = {2895--2929},
  volume                   = 43
}

@Article{RibComTor2011:omega,
  title =        "An iterated greedy algorithm for the flowshop
                  scheduling problem with blocking",
  journal =      omega,
  volume =       39,
  number =       3,
  pages =        "293 -- 301",
  year =         2011,
  author =       "Imma Ribas and Ramon Companys and Xavier
                  Tort-Martorell",
}

@Article{RibComTor2013,
  author =       {Imma Ribas and Ramon Companys and Xavier Tort-Martorell},
  title =        {An Efficient Iterated Local Search Algorithm for the Total Tardiness Blocking Flow Shop Problem},
  journal =      ijpr,
  year =         2013,
  volume =       51,
  number =       17,
  pages =        {5238--5252}
}

@Article{RibUrr2007,
  author =       Ribeiro #and# {Sebasti{\'a}n Urrutia},
  title =        {Heuristics for the Mirrored Traveling Tournament Problem},
  journal =      ejor,
  year =         2007,
  volume =       179,
  number =       3,
  pages =        {775--787}
}

@Article{RicBea2004:joh,
  author =       {A. J. Richmond} #and# Beasley,
  title =        {An Iterative Construction Heuristic for the Ore Selection Problem},
  journal =      joh,
  year =         2004,
  volume =       10,
  number =       2,
  pages =        {153--167}
}

@Article{Rice1976,
  author =       {John R. Rice},
  title =        {The Algorithm Selection Problem},
  journal =       acom,
  year =         1976,
  volume =       15,
  pages =        {65--118},
  doi =          {10.1016/S0065-2458(08)60520-3},
  abstract =     {The problem of selecting an effective algorithm arises in a
                  wide variety of situations. This chapter starts with a
                  discussion on abstract models: the basic model and associated
                  problems, the model with selection based on features, and the
                  model with variable performance criteria. One objective of
                  this chapter is to explore the applicability of the
                  approximation theory to the algorithm selection
                  problem. There is an intimate relationship here and that the
                  approximation theory forms an appropriate base upon which to
                  develop a theory of algorithm selection methods. The
                  approximation theory currently lacks much of the necessary
                  machinery for the algorithm selection problem. There is a
                  need to develop new results and apply known techniques to
                  these new circumstances. The final pages of this chapter form
                  a sort of appendix, which lists 15 specific open problems and
                  questions in this area. There is a close relationship between
                  the algorithm selection problem and the general optimization
                  theory. This is not surprising since the approximation
                  problem is a special form of the optimization problem. Most
                  realistic algorithm selection problems are of moderate to
                  high dimensionality and thus one should expect them to be
                  quite complex. One consequence of this is that most
                  straightforward approaches (even well-conceived ones) are
                  likely to lead to enormous computations for the best
                  selection. The single most important part of the solution of
                  a selection problem is the appropriate choice of the form for
                  selection mapping. It is here that theories give the least
                  guidance and that the art of problem solving is most
                  crucial.}
}

@Article{RivAfrPri2015:coa,
  author =       {Juan Carlos Rivera and H. Murat Afsar} #and# Prins,
  title =        {A Multistart Iterated Local Search for the Multitrip Cumulative Capacitated
               Vehicle Routing Problem},
  journal =      coa,
  year =         2015,
  volume =       61,
  number =       1,
  pages =        {159--187}
}

@Article{RivYanLop2021tweet,
  author       = Rivadeneira_L #and# Yang_JB #and# Lopez-Ibanez,
  title        = {Predicting tweet impact using a novel evidential reasoning
                  prediction method},
  journal      = eswa,
  year         = 2021,
  volume       = 169,
  pages        = 114400,
  month        = may,
  doi          = {10.1016/j.eswa.2020.114400},
  abstract     = {This study presents a novel evidential reasoning (ER)
                  prediction model called MAKER-RIMER to examine how different
                  features embedded in Twitter posts (tweets) can predict the
                  number of retweets achieved during an electoral campaign. The
                  tweets posted by the two most voted candidates during the
                  official campaign for the 2017 Ecuadorian Presidential
                  election were used for this research. For each tweet, five
                  features including type of tweet, emotion, URL, hashtag, and
                  date are identified and coded to predict if tweets are of
                  either high or low impact. The main contributions of the new
                  proposed model include its suitability to analyse tweet
                  datasets based on likelihood analysis of data. The model is
                  interpretable, and the prediction process relies only on the
                  use of available data. The experimental results show that
                  MAKER-RIMER performed better, in terms of misclassification
                  error, when compared against other predictive machine
                  learning approaches. In addition, the model allows observing
                  which features of the candidates' tweets are linked to high
                  and low impact. Tweets containing allusions to the contender
                  candidate, either with positive or negative connotations,
                  without hashtags, and written towards the end of the
                  campaign, were persistently those with the highest
                  impact. URLs, on the other hand, is the only variable that
                  performs differently for the two candidates in terms of
                  achieving high impact. MAKER-RIMER can provide campaigners of
                  political parties or candidates with a tool to measure how
                  features of tweets are predictors of their impact, which can
                  be useful to tailor Twitter content during electoral
                  campaigns.},
  keywords     = {Evidential reasoning rule,Belief rule-based inference,Maximum
                  likelihood data analysis,Twitter,Retweet,Prediction}
}

@Article{Rob1995statcomp,
  author =       {Robert, C. P.},
  title =        {Simulation of truncated normal variables},
  journal =      statscomp,
  year =         1995,
  volume =       5,
  number =       2,
  pages =        {121--125},
  month =        jun,
}

@Article{RomKraArn2012protein,
  author =       {P. A. Romero and A. Krause and F. H. Arnold},
  title =        {Navigating the Protein Fitness Landscape with {Gaussian}
                  Processes},
  doi =          {10.1073/pnas.1215251110},
  year =         2012,
  month =        dec,
  volume =       110,
  number =       3,
  pages =        {E193--E201},
  journal =      pnas,
  keywords =     "Combinatorial Black-box Expensive"
}

@Article{RomSan1991,
  title =        {A Theoretical Framework for Simulated Annealing},
  author =       Romeo_F #and# Sangiovanni-Vincentelli_A,
  journal =      alg,
  volume =       6,
  number =       {1-6},
  pages =        {302--345},
  year =         1991,
  publisher =    springer
}

@Article{Roos2001science,
  title =        {Bioinformatics--trying to swim in a sea of data},
  author =       {Roos, David S.},
  journal =      science,
  volume =       291,
  number =       5507,
  pages =        {1260--1261},
  year =         2001,
  publisher =    {American Association for the Advancement of Science}
}

@Article{RopPis2006:ejor,
  author = 	 Ropke #and# Pisinger,
  title = 	 {A Unified Heuristic for a Large Class of Vehicle Routing Problems
               with Backhauls},
  journal = 	 ejor,
  year = 	 2006,
  volume = 	 171,
  number = 	 3,
  pages = 	 {750--775}
}

@Article{RopPis2006:ts,
  author =       Ropke #and# Pisinger,
  title =        {An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problme with Time Windows},
  journal =      ts,
  year =         2006,
  volume =       40,
  number =       4,
  pages =        {455--472}
}

@Article{Ros2014mutual,
  title =        {Mutual Information between Discrete and Continuous Data Sets},
  author =       {Ross, Brian C.},
  doi =          {10.1371/journal.pone.0087357},
  journal =      pone,
  publisher =    {Public Library of Science},
  year =         2014,
  month =        feb,
  volume =       9,
  pages =        {1--5},
  number =       2,
  abstract =     {Mutual information (MI) is a powerful method for detecting
                  relationships between data sets. There are accurate methods
                  for estimating MI that avoid problems with ``binning'' when
                  both data sets are discrete or when both data sets are
                  continuous. We present an accurate, non-binning MI estimator
                  for the case of one discrete data set and one continuous data
                  set. This case applies when measuring, for example, the
                  relationship between base sequence and gene expression level,
                  or the effect of a cancer drug on patient survival time. We
                  also show how our method can be adapted to calculate the
                  Jensen-Shannon divergence of two or more data sets.},
}

@Article{RosKleWol1990,
  title =        {Temperature measurement and equilibrium dynamics of simulated annealing placements},
  author =       {Rose, Jonathan and Klebsch, Wolfgang and Wolf, J{\"u}rgen},
  journal =      tcad,
  volume =       9,
  number =       3,
  pages =        {253--259},
  year =         1990,
}

@Article{Rothberg2007,
  title =        {An evolutionary algorithm for polishing mixed integer programming solutions},
  author =       {Rothberg, Edward},
  journal =      informsjoc,
  volume =       19,
  number =       4,
  pages =        {534--541},
  year =         2007,
  publisher =    informs
}

@Article{Rothman1985,
  title =        {Nonlinear inversion, statistical mechanics, and residual statics estimation},
  author =       {Rothman, Daniel H.},
  journal =      {Geophysics},
  volume =       50,
  number =       12,
  pages =        {2784--2796},
  year =         1985,
  publisher =    {Society of Exploration Geophysicists}
}

@Article{Rothman1986,
  title =        {Automatic estimation of large residual statics corrections},
  author =       {Rothman, Daniel H.},
  journal =      {Geophysics},
  volume =       51,
  number =       2,
  pages =        {332--346},
  year =         1986,
  publisher =    {Society of Exploration Geophysicists}
}

@Article{Roy2010:ejor,
  author =       Roy,
  title =        "Robustness in operational research and decision
                  aiding: A multi-faceted issue",
  journal =      ejor,
  volume =       200,
  number =       3,
  pages =        "629--638",
  year =         2010,
  doi =          "10.1016/j.ejor.2008.12.036",
}

@Article{RudCapRou2023improved_pnb,
  author       = Rudich_I #and# Cappart_Q #and# Rousseau_LM,
  title        = {Improved {Peel}-and-{Bound}: Methods for Generating Dual Bounds
                  with Multivalued Decision Diagrams},
  journal      = jair,
  year         = 2023,
  volume       = 77,
  pages        = {1489--1538},
  month        = aug,
  doi          = {10.1613/jair.1.14607},
  publisher    = {{AI} Access Foundation}
}

@Article{RudSchGri2016coa,
  author =       Rudolph_G #and# Schutze_O #and# {Christian Grimme} #and#
                  {Christian Dom{\'i}nguez-Medina} #and# Trautmann,
  title =        "Optimal averaged {Hausdorff} archives for bi-objective
                  problems: theoretical and numerical results",
  journal =      coa,
  volume =       64,
  number =       2,
  year =         2016,
  pages =        {589--618},
}

@Article{Rudolph1994:tnn,
  title =        {Convergence analysis of canonical genetic algorithms},
  author =       Rudolph_G,
  journal =      tnn,
  volume =       5,
  number =       1,
  pages =        {96--101},
  year =         1994
}

@Article{RuiMar05,
  author =       Ruiz_Ruben #and# Maroto,
  title =        "A Comprehensive Review and Evaluation of Permutation
                  Flow\-shop Heuristics",
  journal =      ejor,
  year =         2005,
  volume =       165,
  number =       2,
  pages =        {479--494},
}

@Article{RuiMar06,
  author     =   Ruiz_Ruben #and# Maroto #and# {Javier Alcaraz},
  title          =  {Two new robust genetic algorithms for the flowshop scheduling problem},
  journal     =  omega,
  publisher = elsevier,
  volume    =  34,
  number   = 5,
  pages      = {461--476},
  year        =  2006,
  doi          = {10.1016/j.omega.2004.12.006}
}

@Article{RuiSabLuq2015wasfga,
  author       = Ruiz_AB #and# Saborido_R #and# Luque_M,
  title        = {A preference-based evolutionary algorithm for multiobjective
                  optimization: the weighting achievement scalarizing function
                  genetic algorithm},
  journal      = jgo,
  year         = 2015,
  volume       = 62,
  number       = 1,
  pages        = {101--129},
  month        = may,
  annote       = {Proposed WASF-GA},
  doi          = {10.1007/s10898-014-0214-y},
  abstract     = {When solving multiobjective optimization problems,
                  preference-based evolutionary multiobjective optimization
                  (EMO) algorithms introduce preference information into an
                  evolutionary algorithm in order to focus the search for
                  objective vectors towards the region of interest of the
                  Pareto optimal front. In this paper, we suggest a
                  preference-based EMO algorithm called weighting achievement
                  scalarizing function genetic algorithm (WASF-GA), which
                  considers the preferences of the decision maker (DM)
                  expressed by means of a reference point. The main purpose of
                  WASF-GA is to approximate the region of interest of the
                  Pareto optimal front determined by the reference point, which
                  contains the Pareto optimal objective vectors that obey the
                  preferences expressed by the DM in the best possible way. The
                  proposed approach is based on the use of an achievement
                  scalarizing function (ASF) and on the classification of the
                  individuals into several fronts. At each generation of
                  WASF-GA, this classification is done according to the values
                  that each solution takes on the ASF for the reference point
                  and using different weight vectors. These vectors of weights
                  are selected so that the vectors formed by their inverse
                  components constitute a well-distributed representation of
                  the weight vectors space. The efficiency and usefulness of
                  WASF-GA is shown in several test problems in comparison to
                  other preference-based EMO algorithms. Regarding a metric
                  based on the hypervolume, we can say that WASF-GA has
                  outperformed the other algorithms considered in most of the
                  problems.}
}

@Article{RuiStu04:ejor,
  author =       Ruiz_Ruben #and# Stuetzle,
  title =        {A Simple and Effective Iterated Greedy Algorithm for
                  the Permutation Flowshop Scheduling Problem},
  journal =      ejor,
  year =         2007,
  volume =       177,
  number =       3,
  pages =        {2033--2049}
}

@Article{RuiStu2008:ejor,
  author =       Ruiz_Ruben #and# Stuetzle,
  title =        "An {Iterated} {Greedy} heuristic for the sequence
                  dependent setup times flowshop problem with makespan
                  and weighted tardiness objectives ",
  journal =      ejor,
  volume =       187,
  number =       3,
  pages =        "1143 -- 1159",
  year =         2008,
}

@Article{Russell95,
  author =       {Robert A. Russell},
  title =        {Hybrid Heuristics for the Vehicle Routing Problem with Time Windows},
  journal =      ts,
  year =         1995,
  volume =       29,
  number =       2,
  pages =        {156--166}
}

@Article{SabAyoKen2013tec,
  author = {N. R. {Sabar} and M. {Ayob}} #and# Kendall_G #and# {R. {Qu}},
  journal= tec,
  title  = {Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems},
  year   = 2013,
  volume = 17,
  number = 6,
  pages  = {840--861}
}

@Article{SabAyoKen2015tcyb,
  author  = {N. R. {Sabar} and M. {Ayob}} #and# Kendall_G #and# {R. {Qu}},
  journal = tcyb,
  title   = {A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems},
  year    = 2015,
  volume  = 45,
  number  = 2,
  pages   = {217--228}
}

@Article{SabAyoKen2015tec,
  author  = {N. R. Sabar and M. Ayob} #and# Kendall_G #and# {R. Qu},
  journal = tec,
  title   = {Automatic Design of a Hyper-Heuristic Framework With Gene Expression Programming for Combinatorial Optimization Problems},
  year    = 2015,
  volume  = 19,
  number  = 3,
  pages   = {309--325}
}

@Article{SacDuvMai2018ego-ls-svm,
  title =        {A classification approach to efficient global optimization in
                  presence of non-computable domains},
  author =       {Sacher, Matthieu and Duvigneau, R{\'e}gis and Le Maitre,
                  Olivier and Durand, Mathieu and Berrini, Elisa and Hauville,
                  Fr{\'e}d{\'e}ric and Astolfi, Jacques-Andr{\'e}},
  journal =      smo,
  volume =       58,
  number =       4,
  pages =        {1537--1557},
  year =         2018,
  publisher =    {Springer},
  doi =          {10.1007/s00158-018-1981-8},
  keywords =     "Safe optimization; CMA-ES, Gaussian processes; Least-Squares
                  Support Vector Machine",
  annote =       "Proposed EGO-LS-SVM"
}

@Article{SadFow2012nosql,
  title =        {{NoSQL} distilled},
  author =       {Sadalage, Pramod J. and Fowler, Martin},
  journal =      {AddisonWesley Professional},
  year =         2012
}

@Article{SakarWQ00,
  author =       Sakarya #and# Mays,
  title =        "Optimal Operation of Water Distribution Pumps Considering
                  Water Quality",
  journal =      jwrpm,
  volume =       126,
  number =       4,
  pages =        {210--220},
  date =         "2000-07/2000-08",
  year =         2000,
  month =        jul # " / " # aug,
}

@Article{SamPelDarRodPac2016ant,
  author =       {Marcela Sam\`a} #and#  Pellegrini #and# {Andrea D'Ariano and Joaquin Rodriguez and Dario Pacciarelli},
  title =        "Ant colony optimization for the real-time train routing selection problem",
  journal =      trb,
  volume =       85,
  pages =        {89--108},
  year =         2016,
  doi =          {10.1016/j.trb.2016.01.005},
  keywords =     {irace},
}

@Article{Sambridge1999,
  title =        {Geophysical inversion with a neighbourhood algorithm--I. Searching a parameter space},
  author =       {Sambridge, Malcolm},
  journal =      {Geophysical Journal International},
  volume =       138,
  number =       2,
  pages =        {479--494},
  year =         1999
}

@Article{SanDorNeb2019fame,
  title =        {A novel multi-objective evolutionary algorithm with fuzzy
                  logic based adaptive selection of operators: {FAME}},
  journal =      is,
  volume =       471,
  pages =        {233--251},
  year =         2019,
  issn =         {0020-0255},
  doi =          {10.1016/j.ins.2018.09.005},
  author =       {Alejandro Santiago} #and#  Dorronsoro #and# Nebro #and# Durillo_JJ #and# {Oscar Castillo} #and# {H{\'e}ctor
                  J. Fraire},
  keywords =     {Multi-objective optimization, density estimation,
                  evolutionary algorithm, adaptive algorithm, fuzzy logic, spatial spread deviation},
}

@Article{SanGalRub2008tec,
  author       = {S\'anchez, Javier and Gal\'an, Manuel and Rubio, Enrique},
  title        = {Applying a traffic lights evolutionary optimization technique
                  to a real case: ``{Las} {Ramblas}'' area in {Santa} {Cruz} de
                  {Tenerife}},
  journal      = tec,
  year         = 2008,
  volume       = 12,
  number       = 1,
  pages        = {25--40},
  keywords     = {Cellular automata, Combinatorial optimization, Genetic
                  algorithms, Microscopic traffic simulator, Traffic lights
                  optimization}
}

@Article{SanGalRub2010tec,
  author       = {J. J. S\'anchez-Medina and M. J. Gal\'an-Moreno and
                  E. Rubio-Royo},
  title        = {Traffic Signal Optimization in ``{La} {Almozara}'' District
                  in {Saragossa} Under Congestion Conditions, Using Genetic
                  Algorithms, Traffic Microsimulation, and Cluster Computing},
  journal      = ieee-tits,
  year         = 2010,
  volume       = 11,
  number       = 1,
  pages        = {132--141},
  month        = mar,
  keywords     = {cellular automata; genetic algorithms; road traffic;traffic
                  light programming;urban traffic congestion},
  doi          = {10.1109/TITS.2009.2034383},
  issn         = {1524-9050}
}

@Article{SanOst2018wrr,
  title =        {Stochastic Scenario Evaluation in Evolutionary Algorithms
                  Used for Robust Scenario-Based Optimization},
  author =       {Sankary, Nathan} #and# Ostfeld,
  journal =      wrr,
  volume =       54,
  number =       4,
  pages =        {2813--2833},
  year =         2018,
}

@Article{SanRopHva2018joh,
  author =       {Alberto Santini and Stefan Ropke and Hvattum, Lars Magnus},
  title =        {A comparison of acceptance criteria for the adaptive large neighbourhood search metaheuristic},
  journal =      joh,
  volume =       24,
  pages =        "783--815",
  year =         2018,
  doi =          {10.1007/s10732-018-9377-x}
}

@Article{Sandgren1988,
  author =       {Sandgren, E.},
  title =        {Nonlinear integer and discrete programming in mechanical
                  design optimization},
  volume =       112,
  number =       2,
  journal =      {Journal of Mechanical Design},
  pages =        "223--229",
  year =         1990,
  doi =          {10.1115/1.2912596}
}

@Article{SasBerBie2022deepcave,
  author       = {Sass, René} #and# {Bergman, Eddie} #and# Biedenkapp #and#
                  Hutter #and# Lindauer_M,
  title        = {{DeepCAVE}: An Interactive Analysis Tool for Automated
                  Machine Learning},
  journal      = arxiv # {2206.03493 [cs.LG]},
  year         = 2022,
  doi          = {10.48550/arXiv.2206.03493}
}

@Article{Savelsbergh85tw,
  title =        {Local search in routing problems with time windows},
  volume =       4,
  doi =         {10.1007/BF02022044},
  abstract =     {We develop local search algorithms for routing
                  problems with time windows. The presented algorithms
                  are based on thek-interchange concept. The presence
                  of time windows introduces feasibility constraints,
                  the checking of which normally requires O(N)
                  time. Our method reduces this checking effort to
                  O(1) time. We also consider the problem of finding
                  initial solutions. A complexity result is given and
                  an insertion heuristic is described.},
  number =       1,
  journal =      aor,
  author =       Savelsbergh_MWP,
  month =        dec,
  year =         1985,
  pages =        {285--305}
}

@Article{SaxDurDebZha2013pca,
  title =        {Objective Reduction in Many-Objective Optimization: Linear
                  and Nonlinear Algorithms},
  author =       Saxena_DK #and# Duro_JA #and# {Tiwari, Anish} #and# Deb #and#
                  Zhang_Qingfu,
  journal =      tec,
  volume =       17,
  number =       1,
  pages =        {77--99},
  year =         2013,
  doi =          {10.1109/TEVC.2012.2185847}
}

@Article{SchDoeHar09,
  author =       Schilde #and# Doerner #and# Hartl #and# "Guenter
                  Kiechle",
  title =        {Metaheuristics for the bi-objective orienteering
                  problem},
  number =       3,
  journal =      swarm,
  year =         2009,
  pages =        {179--201},
  volume =       3,
  doi =          {10.1007/s11721-009-0029-5},
  abstract =     {In this paper, heuristic solution
                  techniques for the multi-objective orienteering
                  problem are developed. The motivation stems from the
                  problem of planning individual tourist routes in a
                  city. Each point of interest in a city provides
                  different benefits for different categories (e.g.,
                  culture, shopping). Each tourist has different
                  preferences for the different categories when
                  selecting and visiting the points of interests
                  (e.g., museums, churches). Hence, a multi-objective
                  decision situation arises.  To determine all the
                  Pareto optimal solutions, two metaheuristic search
                  techniques are developed and applied. We use the
                  Pareto ant colony optimization algorithm and extend
                  the design of the variable neighborhood search
                  method to the multi-objective case. Both methods are
                  hybridized with path relinking procedures. The
                  performances of the two algorithms are tested on
                  several benchmark instances as well as on real world
                  instances from different Austrian regions and the
                  cities of Vienna and Padua.  The computational
                  results show that both implemented methods are well
                  performing algorithms to solve the multi-objective
                  orienteering problem.},
}

@Article{SchEgeBan2009aco,
  title =        {Extended ant colony optimization for non-convex mixed integer
                  nonlinear programming},
  author =       Schluter_M #and# Egea_JA #and# Banga_JR,
  journal =      cor,
  volume =       36,
  number =       7,
  pages =        {2217--2229},
  year =         2009,
  doi = {10.1016/j.cor.2008.08.015}
}

@Article{SchEsqLarCoe2012tec,
  author =       Schutze_O #and#  {X. Esquivel and A. Lara} #and# Coello,
  journal =      tec,
  title =        {Using the Averaged {Hausdorff} Distance as a Performance
                  Measure in Evolutionary Multiobjective Optimization},
  year =         2012,
  volume =       16,
  number =       4,
  pages =        {504--522},
}

@Article{SchHan79,
  author    =    {Josef Schmee and Gerald J. Hahn},
  title     =    {A Simple Method for Regression Analysis with Censored Data},
  journal   =    techms,
  year      =    1979,
  volume    =    21,
  number    =    4,
  pages     =    {417--432},
  publisher =    taylorfrancis,
  doi       =    {10.2307/1268280}
}

@Article{SchHarSka2017safe,
  title =        {Safe active learning and safe {Bayesian} optimization for
                  tuning a {PI}-controller},
  author =       {Schillinger, Mark and Hartmann, Benjamin and Skalecki, Patric
                  and Meister, Mona and Nguyen-Tuong, Duy and Nelles, Oliver},
  journal =      ifac,
  volume =       50,
  number =       1,
  pages =        {5967--5972},
  year =         2017,
  doi =          {10.1016/j.ifacol.2017.08.1258},
  publisher =    {Elsevier}
}

@Article{SchHenSie2004hiv,
  author =       {Julie R. Schames and Richard H. Henchman and Jay S. Siegel
                  and Christoph A. Sotriffer and Haihong Ni and J. Andrew
                  McCammon},
  title =        {Discovery of a Novel Binding Trench in {HIV} Integrase},
  journal =      {Journal of Medicinal Chemistry},
  volume =       47,
  number =       8,
  pages =        {1879--1881},
  year =         2004,
  doi =          {10.1021/jm0341913},
  annote =       "Evolutionary optimization of the first clinically approved
                  anti-viral drug for HIV"
}

@Article{SchHerTal2019archiver,
  title =        {Archivers for the representation of the set of approximate
                  solutions for {MOPs}},
  author =       Schutze_O #and# Hernandez_C #and# Talbi #and# {Sun, Jian-Qiao
                  and Naranjani, Yousef and Xiong, F-R},
  journal =      joh,
  year =         2019,
  pages =        {71--105},
  volume =       25,
  doi =          "10.1007/s10732-018-9383-z",
  keywords =     "archiving, nearly optimality, epsilon-dominance, epsilon-approximation, hausdorff convergence",

}

@Article{SchKoe2009,
  year =         2009,
  volume =       123,
  number =       4,
  pages =        {421--433},
  author =       {Jeffrey C. Schank and Thomas J. Koehnle},
  title =        {Pseudoreplication is a pseudoproblem},
  journal =      {Journal of Comparative Psychology}
}

@Article{SchLarCoe2011tec,
  author =       Schutze_O #and# {A. Lara} #and# Coello,
  journal =      tec,
  title =        {On the Influence of the Number of Objectives on the Hardness
                  of a Multiobjective Optimization Problem},
  year =         2011,
  volume =       15,
  number =       4,
  pages =        {444--455}
}

@Article{SchLauCoeDelTal2008,
  title =        {Convergence of stochastic search algorithms to finite size
                  {Pareto} set approximations},
  author =       Schutze_O #and# Laumanns #and# Coello #and# {Dellnitz,
                  Michael} #and# Talbi,
  journal =      jgo,
  volume =       41,
  number =       4,
  pages =        {559--577},
  year =         2008,
}

@Article{SchLauTanCoeTal2010,
  title =        {Computing gap free {Pareto} front approximations with
                  stochastic search algorithms},
  author =       Schutze_O #and# Laumanns #and# {Emilia Tantar} #and# Coello
                  #and# Talbi,
  journal =      ec,
  volume =       18,
  number =       1,
  pages =        {65--96},
  year =         2010,
}

@Article{SchMar1999,
  author =       {G. R. Schreiber} #and# Martin_O,
  title =        {Cut Size Statistics of Graph Bisection Heuristics},
  journal =      siamo,
  year =         1999,
  volume =       10,
  number =       1,
  pages =        {231--251}
}

@Article{SchSchStaDue2000,
  title =        "Record Breaking Optimization Results Using the Ruin
                  and Recreate Principle",
  journal =      jcp,
  volume =       159,
  number =       2,
  pages =        "139--171",
  year =         2000,
  author =       "Gerhard Schrimpf and Schneider, Johannes and Hermann
                  Stamm-Wilbrandt and Gunter Dueck"
}

@Article{SchSchoTho2019min,
  title =        {Min-ordering and max-ordering scalarization methods for
                  multi-objective robust optimization},
  author =       {Schmidt, Marie} #and# Schobel_A #and# {Thom, Lisa},
  journal =      ejor,
  volume =       275,
  number =       2,
  pages =        {446--459},
  year =         2019,
  publisher =    {Elsevier}
}

@Article{SchSpeKra2018gptut,
  author =       {Schulz, Eric and Speekenbrink, Maarten and Krause, Andreas},
  year =         2018,
  month =        aug,
  pages =        {1--16},
  title =        {A tutorial on {Gaussian} process regression: {Modelling},
                  exploring, and exploiting functions},
  volume =       85,
  journal =      jmatpsy,
  doi =          {10.1016/j.jmp.2018.03.001}
}

@Article{SchStu2004:jmma,
  author = 	 Schiavinotto #and# Stuetzle,
  title = 	 {The Linear Ordering Problem: Instances, Search Space Analysis
  and Algorithms},
  journal = 	 jmma,
  year = 	 2004,
  volume = 	 3,
  number = 	 4,
  pages = 	 {367--402}
}

@Article{SchStu2007:cor,
  author =       Schiavinotto #and# Stuetzle,
  title =        {A Review of Metrics on Permutations for Search Space Analysis},
  journal =       cor,
  year =         2007,
  volume =       34,
  number =       10,
  pages =        {3143--3153}}

@Article{SchTacWuiSamStu2013,
  author = 	 {Tom Schrijvers and Guido Tack and Pieter Wuille and Horst Samulowitz and Peter J. Stuckey},
  title = 	 {Search Combinators},
  journal = 	 constr,
  year = 	 2013,
  volume = 	 18,
  number = 	 2,
  pages = 	 {269--305}
}

@Article{SchVasCoe2011space,
  author       = Schutze_O #and# {Massimiliano Vasile} #and# Coello,
  title        = {Computing the Set of Epsilon-Efficient Solutions in
                  Multiobjective Space Mission Design},
  journal      = {Journal of Aerospace Computing, Information, and
                  Communication},
  year         = 2011,
  volume       = 8,
  number       = 3,
  pages        = {53--70},
  doi          = {10.2514/1.46478},
  publisher    = {American Institute of Aeronautics and Astronautics ({AIAA})}
}

@Article{SchWelJon1998,
  author       = {Matthias Schonlau and William J. Welch and Donald R. Jones},
  title        = {Global versus Local Search in Constrained Optimization of
                  Computer Models},
  journal      = {Lecture Notes-Monograph Series},
  year         = 1998,
  volume       = 34,
  pages        = {11--25},
  editor       = {Nancy Flournoy and William F. Rosenberger and Weng Kee Wong},
  publisher    = {Institute of Mathematical Statistics},
  doi          = {10.2307/4356058}
}

@Article{ScheBraTor2022jair,
  author       = {Schede, Elias and Brandt, Jasmin and Tornede, Alexander and
                  Wever, Marcel and Bengs, Viktor} #and# Huellermeier #and#
                  Tierney,
  title        = {A survey of methods for automated algorithm configuration},
  journal      = jair,
  year         = 2022,
  volume       = 75,
  pages        = {425--487},
  doi          = {10.1613/jair.1.13676}
}

@Article{Scipy2020natmet,
  fullauthor =   {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
                  Haberland, Matt and Reddy, Tyler and Cournapeau, David and
                  Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren
                  and Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
                  Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
                  Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
                  Kern, Robert and Larson, Eric and Carey, C J and Polat,
                  {\.I}lhan and Feng, Yu and Moore, Eric W. and {VanderPlas},
                  Jake and Laxalde, Denis and Perktold, Josef and Cimrman,
                  Robert and Henriksen, Ian and Quintero, E. A. and Harris,
                  Charles R. and Archibald, Anne M. and Ribeiro, Ant{\^o}nio
                  H. and Pedregosa, Fabian and {van Mulbregt}, Paul and {SciPy
                  1.0 Contributors}},
  author =       "Virtanen, Pauli and others",
  title =        {{SciPy} 1.0: Fundamental Algorithms for Scientific Computing
                  in {Python}},
  journal =      naturemethods,
  year =         2020,
  volume =       17,
  pages =        {261--272},
  epub = "https://rdcu.be/b08Wh",
  doi =          {10.1038/s41592-019-0686-2},
}

@Article{Sha1970bfgs,
  author       = {David F. Shanno},
  title        = {Conditioning of Quasi-Newton Methods for Function
                  Minimization},
  journal      = mathcom,
  year         = 1970,
  volume       = 24,
  number       = 111,
  pages        = {647--656},
  annote       = {One of the four papers that proposed BFGS.},
  publisher    = ams-pub,
  issn         = {00255718, 10886842},
  eprint       = {http://www.jstor.org/stable/2004840},
  keywords     = {BFGS}
}

@Article{ShaLopAlm2023hidden,
  author       = Shavarani_Mahdi #and# Lopez-Ibanez #and# Allmendinger,
  title        = {Detecting Hidden and Irrelevant Objectives in Interactive
                  Multi-Objective Optimization},
  journal      = tec,
  year         = 2023,
  volume       = 28,
  number       = 2,
  pages        = {544--557},
  doi          = {10.1109/TEVC.2023.3269348},
  abstract     = {Evolutionary multi-objective optimization algorithms (EMOAs)
                  typically assume that all objectives that are relevant to the
                  decision-maker (DM) are optimized by the EMOA. In some
                  scenarios, however, there are irrelevant objectives that are
                  optimized by the EMOA but ignored by the DM, as well as,
                  hidden objectives that the DM considers when judging the
                  utility of solutions but are not optimized. This discrepancy
                  between the EMOA and the DM's preferences may impede the
                  search for the most-preferred solution and waste resources
                  evaluating irrelevant objectives. Research on objective
                  reduction has focused so far on the structure of the problem
                  and correlations between objectives and neglected the role of
                  the DM. We formally define here the concepts of irrelevant
                  and hidden objectives and propose methods for detecting them,
                  based on uni-variate feature selection and recursive feature
                  elimination, that use the preferences already elicited when a
                  DM interacts with a ranking-based interactive EMOA
                  (iEMOA). We incorporate the detection methods into an iEMOA
                  capable of dynamically switching the objectives being
                  optimized. Our experiments show that this approach can
                  efficiently identify which objectives are relevant to the DM
                  and reduce the number of objectives being optimized, while
                  keeping and often improving the utility, according to the DM,
                  of the best solution found.}
}

@Article{ShaLopKno2023bench,
  author       = Shavarani_Mahdi #and# Lopez-Ibanez #and# Knowles,
  title        = {On Benchmarking Interactive Evolutionary Multi-Objective
                  Algorithms},
  journal      = tec,
  year         = 2023,
  volume       = 28,
  number       = 4,
  pages        = {1084--1098},
  doi          = {10.1109/TEVC.2023.3289872},
  abstract     = {We carry out a detailed performance assessment of two
                  interactive evolutionary multi-objective algorithms (EMOAs)
                  using a machine decision maker that enables us to repeat
                  experiments and study specific behaviours modeled after human
                  decision makers (DMs). Using the same set of benchmark test
                  problems as in the original papers on these interactive EMOAs
                  (in up to 10 objectives), we bring to light interesting
                  effects when we use a machine DM based on sigmoidal utility
                  functions that have support from the psychology literature
                  (replacing the simpler utility functions used in the original
                  papers). Our machine DM enables us to go further and simulate
                  human biases and inconsistencies as well. Our results from
                  this study, which is the most comprehensive assessment of
                  multiple interactive EMOAs so far conducted, suggest that
                  current well-known algorithms have shortcomings that need
                  addressing. These results further demonstrate the value of
                  improving the benchmarking of interactive EMOAs}
}

@Article{ShaLopMie2021visual,
  title =        "Visualizations for Decision Support in Scenario-based
                  Multiobjective Optimization",
  author =       Shavazipour #and# Lopez-Ibanez #and# Miettinen,
  journal =      is,
  volume =       578,
  pages =        {1--21},
  year =         2021,
  abstract =     "We address challenges of decision problems when managers need
                  to optimize several conflicting objectives simultaneously
                  under uncertainty. We propose visualization tools to support
                  the solution of such scenario-based multiobjective
                  optimization problems. Suitable graphical visualizations are
                  necessary to support managers in understanding, evaluating,
                  and comparing the performances of management decisions
                  according to all objectives in all plausible scenarios. To
                  date, no appropriate visualization has been suggested. This
                  paper fills this gap by proposing two visualization methods:
                  a novel extension of empirical attainment functions for
                  scenarios and an adapted version of heatmaps. They help a
                  decision-maker in gaining insight into realizations of
                  trade-offs and comparisons between objective functions in
                  different scenarios. Some fundamental questions that a
                  decision-maker may wish to answer with the help of
                  visualizations are also identified. Several examples are
                  utilized to illustrate how the proposed visualizations
                  support a decision-maker in evaluating and comparing
                  solutions to be able to make a robust decision by answering
                  the questions. Finally, we validate the usefulness of the
                  proposed visualizations in a real-world problem with a real
                  decision-maker. We conclude with guidelines regarding which
                  of the proposed visualizations are best suited for different
                  problem classes.",
  doi =          {10.1016/j.ins.2021.07.025},
  supplement =   "https://doi.org/10.5281/zenodo.5040421"
}

@Article{ShaPiSha2017:asoco,
  author  = Shao_W #and# Pi_D #and# Shao_Z,
  title   = {Memetic algorithm with node and edge histogram for no-idle flow shop scheduling problem to minimize the makespan criterion},
  journal = asoco,
  volume  = 54,
  pages   = {164--182},
  year    = 2017
}

@Article{ShaPiSha2018:cor,
  author  = Shao_W #and# Pi_D #and# Shao_Z,
  title   = {A hybrid discrete teaching-learning based meta-heuristic for solving no-idle flow shop scheduling problem with total tardiness criterion},
  journal = cor,
  volume  = 94,
  pages   = {89--105},
  year    = 2018,
}

@Article{ShaShuIsh2023is,
  doi =          {10.1016/j.ins.2022.11.155},
  year =         2023,
  volume =       622,
  pages =        {755--770},
  author =       {Ke Shang and Tianye Shu} #and# Ishibuchi #and# {Yang Nan and
                  Lie Meng Pang},
  title =        {Benchmarking large-scale subset selection in evolutionary
                  multi-objective optimization},
  journal =      is,
}

@Article{ShaSte2019deep,
  author =       Shavazipour #and# Stewart_TJ,
  title =        "Multi-objective optimisation under deep uncertainty",
  journal =      operatres,
  year =         2019,
  month =        sep,
  abstract =     "This paper presents a scenario-based Multi-Objective
                  structure to handle decision problems under deep
                  uncertainty. Most of the decisions in real-life problems need
                  to be made in the absence of complete knowledge about the
                  consequences of the decision and/or are characterised by
                  uncertainties about the future which is unpredictable. These
                  uncertainties are almost impossible to reduce by gathering
                  more information and are not statistical in
                  nature. Therefore, classical probability-based approaches,
                  such as stochastic programming, do not address these
                  problems; as they require a correctly-defined complete sample
                  space, strong assumptions (e.g. normality), or both. The
                  proposed method extends the concept of two-stage stochastic
                  programming with recourse to address the capability of
                  dealing with deep uncertainty through the use of scenario
                  planning rather than statistical expectation. In this
                  research, scenarios are used as a dimension of preference to
                  avoid problems relating to the assessment and use of
                  probabilities under deep uncertainty. Such scenario-based
                  thinking involved a multi-objective representation of
                  performance under different future conditions as an
                  alternative to expectation. To the best of our knowledge,
                  this is the first attempt of performing a multi-criteria
                  evaluation under deep uncertainty through a structured
                  optimisation model. The proposed structure replacing
                  probabilities (in dynamic systems with deep uncertainties) by
                  aspirations within a goal programming structure. In fact,
                  this paper also proposes an extension of the goal programming
                  paradigm to deal with deep uncertainty. Furthermore, we will
                  explain how this structure can be modelled, implemented, and
                  solved by Goal Programming using some simple, but not
                  trivial, examples. Further discussion and comparisons with
                  some popular existing methods will also provided to highlight
                  the superiorities of the proposed structure.",
  doi =          "10.1007/s12351-019-00512-1",
}

@Article{ShaStrSte2020cce,
  author =       Shavazipour #and# {Jonas Stray} #and# Stewart_TJ,
  title =        "Sustainable planning in sugar-bioethanol supply chain under
                  deep uncertainty: A case study of {South} {African} sugarcane
                  industry",
  journal =      compchemeng,
  volume =       143,
  pages =        107091,
  year =         2020,
  doi =          "10.1016/j.compchemeng.2020.107091",
  keywords =     "Supply chain management, Multi-objective optimisation, Deep
                  uncertainty, Scenario planning, Renewable energy,",
  abstract =     "In this paper, the strategic planning of sugar-bioethanol
                  supply chains (SCs) under deep uncertainty has been addressed
                  by applying a two-stage scenario-based multiobjective
                  optimisation methodology. In practice, the depth of
                  uncertainty is very high, potential outcomes are not
                  precisely enumerable, and probabilities of outcomes are not
                  properly definable. To date, no appropriate framework has
                  been suggested for dealing with deep uncertainty in supply
                  chain management and energy-related problems. This study is
                  the first try to fills this gap. Particularly, the
                  sustainability of the whole infrastructure of the
                  sugar-bioethanol SCs is analysed in such a way that the final
                  solutions are sustainable, robust and adaptable for a broad
                  range of plausible futures. Three objectives are considered
                  in this problem under six uncertain parameters. A case study
                  of South African sugarcane industry is utilised to study and
                  examine the proposed model. The results prove the economic
                  profitability and sustainability of the project."
}

@Article{ShaSweWan2016taking,
  author =       {Shahriari, B. and Swersky, K. and Wang, Z. and Adams, R. P.}
                  #and# DeFreitas_N,
  journal =      prcieee,
  title =        {Taking the human out of the loop: A review of {Bayesian}
                  optimization},
  year =         2016,
  number =       1,
  pages =        {148--175},
  volume =       104,
  publisher =    {IEEE},
}

@Article{ShaSweWanAdaFre2016,
  author = 	 {Bobak Shahriari and Kevin Swersky and Ziyu Wang and Ryan P. Adams} #and# DeFreitas_N,
  title = 	 {Taking the Human Out of the Loop: {A} Review of {Bayesian} Optimization},
  journal = 	 prcieee,
  year = 	 2016,
  volume = 	 104,
  number = 	 1,
  pages = 	 {148--175}
}

@Article{ShiBac2009niching,
  title =        {Niching with derandomized evolution strategies in artificial
                  and real-world landscapes},
  author =       Shir_OM #and# Baeck_Thomas,
  journal =      natcomp,
  volume =       8,
  number =       1,
  pages =        {171--196},
  year =         2009,
  doi =          "10.1007/s11047-007-9065-5",
  publisher =    {Springer}
}

@Article{ShiCebLoz2018space,
  author       = {Shirazi, Abolfazl} #and# Ceberio #and# Lozano_JA,
  title        = {Spacecraft trajectory optimization: A review of models,
                  objectives, approaches and solutions},
  journal      = paerosci,
  year         = 2018,
  volume       = 102,
  pages        = {76--98},
  month        = oct,
  doi          = {10.1016/j.paerosci.2018.07.007},
}

@Article{ShiMarDud2008stat,
  author =       {David Shilane and Jarno Martikainen and Sandrine Dudoit and
                  Seppo J. Ovaska},
  title =        {A general framework for statistical performance comparison of
                  evolutionary computation algorithms},
  journal =      is,
  volume =       178,
  number =       14,
  pages =        {2870--2879},
  year =         2008,
  doi =          {10.1016/j.ins.2008.03.007},
}

@Article{ShiZha2016,
  title =        {The generalization of {Latin} hypercube sampling},
  author =       {Shields, Michael D. and Zhang, Jiaxin},
  journal =      ress,
  year =         2016,
  pages =        {96--108},
  volume =       148,
  alias =        "Shields2016"
}

@Article{ShmHoo05:bmc,
  author =       Shmygelska #and# Hoos,
  title =        "An Ant Colony Optimisation Algorithm for the {2D}
                  and {3D} Hydrophobic Polar Protein Folding Problem",
  journal =      bmc-bioinf,
  year =         2005,
  volume =       6,
  pages =        30,
  doi =          {10.1186/1471-2105-6-30},
  alias =        {ShmHoo05:protein}
}

@Article{SilConRey2023automatic,
  author       = SilvaMunoz #and# {Contreras-Bolton, Carlos and Rey, Carlos and
                  Parada, Victor},
  title        = {Automatic generation of a hybrid algorithm for the maximum
                  independent set problem using genetic programming},
  journal      = asoco,
  year         = 2023,
  pages        = 110474,
  publisher    = {Elsevier},
  doi          = {10.1016/j.asoc.2023.110474}
}

@Article{SilFraBer2021,
  author =       SilvaMunoz #and# Franzin #and# Bersini,
  title =        {Automatic configuration of the {Cassandra} database using irace},
  year =         2021,
  journal =      peerjcs,
  volume =       7,
  pages =        {e634},
  doi =          {10.7717/peerj-cs.634}
}

@Article{SilRit2017:cor,
  author = 	 {Paulo Vitor Silvestrin} #and# Ritt,
  title = 	 {An Iterated Tabu Search for the Multi-compartment Vehicle Routing Problem},
  journal = 	 cor,
  year = 	 2017,
  volume = 	 81,
  pages = 	 {192--202}
}

@Article{SilSubOch2015,
  author =       {Marcos {Melo Silva}} #and# Subramanian #and# Ochi,
  title =        {An Iterated Local Search Heuristic for the Split Delivery Vehicle
               Routing Problem},
  journal =      cor,
  year =         2015,
  volume =       53,
  pages =        {234--249}
}

@Article{SimChaThi2014:swarm,
  author =       {Olivier Simonin and  Fran{\c{c}}ois Charpillet and Eric Thierry},
  title =        {Revisiting wavefront construction with collective agents: an approach to foraging},
  journal =      swarm,
  year =         2014,
  volume =       9,
  number =       2,
  pages =        {113--138},
  doi =          {10.1007/s11721-014-0093-3},
  keywords =     {irace},
}

@Article{SimHarPae2015ecj,
  author =       {Kevin Sim} #and# Hart_E #and# Paechter,
  title =        {A Lifelong Learning Hyper-heuristic Method for Bin Packing},
  volume =       23,
  number =       1,
  pages =        {37--67},
  year =         2015,
  doi =          {10.1162/EVCO_a_00121},
  journal =      ec,
}

@Article{SimNelSim2011phack,
  author =       "Simmons, Joseph P. and Nelson, Leif D. and Simonsohn, Uri",
  title =        "False-Positive Psychology: Undisclosed Flexibility in Data
                  Collection and Analysis Allows Presenting Anything as
                  Significant",
  journal =      "Psychological Science",
  year =         2011,
  url =          "https://ssrn.com/abstract=1850704",
  annote =       "Proposed the term p-hacking"
}

@Article{SimNew1958heur,
  title =        {Heuristic Problem Solving: The Next Advance in Operations
                  Research},
  volume =       6,
  doi =          {10.1287/opre.6.1.1},
  number =       1,
  journal =      or,
  author =       Simon_HA #and# {Newell, Allen},
  year =         1958,
  pages =        {1--10},
}

@Article{SimLebNel2010anchoring,
  author       = {Simmons, Joseph P. and LeBoeuf, Robyn A. and Nelson, Leif D.},
  title        = {The effect of accuracy motivation on anchoring and
                  adjustment: {Do} people adjust from provided anchors?},
  journal      = {Journal of Personality and Social Psychology},
  year         = 2010,
  volume       = 99,
  number       = 6,
  pages        = {917--932},
  issn         = {1939-1315, 0022-3514},
  shorttitle   = {The effect of accuracy motivation on anchoring and
                  adjustment},
  doi          = {10.1037/a0021540},
  abstract     = {Increasing accuracy motivation (e.g., by providing monetary
                  incentives for accuracy) often fails to increase adjustment
                  away from provided anchors, a result that has led researchers
                  to conclude that people do not effortfully adjust away from
                  such anchors. We challenge this conclusion. First, we show
                  that people are typically uncertain about which way to adjust
                  from provided anchors and that this uncertainty often causes
                  people to believe that they have initially adjusted too far
                  away from such anchors (Studies 1a and 1b). Then, we show
                  that although accuracy motivation fails to increase the gap
                  between anchors and final estimates when people are uncertain
                  about the direction of adjustment, accuracy motivation does
                  increase anchor-estimate gaps when people are certain about
                  the direction of adjustment, and that this is true regardless
                  of whether the anchors are provided or self-generated
                  (Studies 2, 3a, 3b, and 5). These results suggest that people
                  do effortfully adjust away from provided anchors but that
                  uncertainty about the direction of adjustment makes that
                  adjustment harder to detect than previously assumed. This
                  conclusion has important theoretical implications, suggesting
                  that currently emphasized distinctions between anchor types
                  (self-generated vs. provided) are not fundamental and that
                  ostensibly competing theories of anchoring (selective
                  accessibility and anchoring-and-adjustment) are
                  complementary.},
  language     = {en}
}

@Article{Simon1955,
  author =       Simon_HA,
  title =        {A Behavioral Model of Rational Choice},
  journal =      qje,
  volume =       69,
  number =       1,
  pages =        {99--118},
  year =         1955,
  epub =         {http://www.jstor.org/stable/1884852}
}

@Article{SinIsaTap2011pareto,
  author =       {Singh, Hemant Kumar and Isaacs, Amitay} #and# Ray_T,
  title =        {A {Pareto} Corner Search Evolutionary Algorithm and
                  Dimensionality Reduction in Many-Objective Optimization
                  Problems},
  journal =      tec,
  year =         2011,
  volume =       15,
  number =       4,
  pages =        {539--556},
  abstract =     {Many-objective optimization refers to the optimization
                  problems containing large number of objectives, typically
                  more than four.  Non-dominance is an inadequate strategy for
                  convergence to the Pareto front for such problems, as almost
                  all solutions in the population become non-dominated,
                  resulting in loss of convergence pressure.  However, for some
                  problems, it may be possible to generate the Pareto front
                  using only a few of the objectives, rendering the rest of the
                  objectives redundant. Such problems may be reducible to a
                  manageable number of relevant objectives, which can be
                  optimized using conventional multiobjective evolutionary
                  algorithms (MOEAs). For dimensionality reduction, most
                  proposals in the paper rely on analysis of a representative
                  set of solutions obtained by running a conventional MOEA for
                  a large number of generations, which is computationally
                  overbearing.  A novel algorithm, Pareto corner search
                  evolutionary algorithm (PCSEA), is introduced in this paper,
                  which searches for the corners of the Pareto front instead of
                  searching for the complete Pareto front. The solutions
                  obtained using PCSEA are then used for dimensionality
                  reduction to identify the relevant objectives. The potential
                  of the proposed approach is demonstrated by studying its
                  performance on a set of benchmark test problems and two
                  engineering examples. While the preliminary results obtained
                  using PCSEA are promising, there are a number of areas that
                  need further investigation. This paper provides a number of
                  useful insights into dimensionality reduction and, in
                  particular, highlights some of the roadblocks that need to be
                  cleared for future development of algorithms attempting to
                  use few selected solutions for identifying relevant
                  objectives},
  doi =          {10.1109/TEVC.2010.2093579},
}

@Article{SinPin1998:IIE,
  author =       {Marcos Singer and Michael L. Pinedo},
  title =        {A Computational Study of Branch and Bound Techniques for Minimizing the Total Weighted Tardiness in Job Shops},
  journal =      iiet,
  year =         1998,
  volume =       30,
  number =       2,
  pages =        {109--118}
}

@Article{SinSaxDeb2013asc,
  author =       {Ankur Sinha} #and# Saxena_DK #and# Deb #and# Tiwari_A,
  title =        {Using objective reduction and interactive procedure to handle
                  many-objective optimization problems},
  journal =      asoco,
  volume =       13,
  number =       1,
  pages =        {415--427},
  year =         2013,
  doi =          {10.1016/j.asoc.2012.08.030},
  keywords =     {Evolutionary algorithms, Evolutionary multi- and
                  many-objective optimization, Multi-criteria decision making,
                  Machine learning, Interactive optimization},
  abstract =     {A number of practical optimization problems are posed as
                  many-objective (more than three objectives) problems. Most of
                  the existing evolutionary multi-objective optimization
                  algorithms, which target the entire Pareto-front are not
                  equipped to handle many-objective problems. Though there have
                  been copious efforts to overcome the challenges posed by such
                  problems, there does not exist a generic procedure to
                  effectively handle them. This paper presents a simplify and
                  solve framework for handling many-objective optimization
                  problems. In that, a given problem is simplified by
                  identification and elimination of the redundant objectives,
                  before interactively engaging the decision maker to converge
                  to the most preferred solution on the Pareto-optimal
                  front. The merit of performing objective reduction before
                  interacting with the decision maker is two fold. Firstly, the
                  revelation that certain objectives are redundant,
                  significantly reduces the complexity of the optimization
                  problem, implying lower computational cost and higher search
                  efficiency. Secondly, it is well known that human beings are
                  not efficient in handling several factors (objectives in the
                  current context) at a time. Hence, simplifying the problem a
                  priori addresses the fundamental issue of cognitive overload
                  for the decision maker, which may help avoid inconsistent
                  preferences during the different stages of interactive
                  engagement. The implementation of the proposed framework is
                  first demonstrated on a three-objective problem, followed by
                  its application on two real-world engineering problems.}
}

@Article{SinBahRay2019distance,
  title =        {Distance-based subset selection for benchmarking in
                  evolutionary multi/many-objective optimization},
  author =       {Singh, Hemant Kumar and Bhattacharjee, Kalyan Shankar} #and# Ray_T,
  journal =      tec,
  year =         2019,
  number =       5,
  pages =        {904--912},
  volume =       23,
  publisher =    {IEEE}
}

@Article{SioGag2018:ejor,
  author  = {Sioud, Aymen} #and# Gagne_C,
  title   = {Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times},
  journal = ejor,
  volume  = 264,
  number  = 1,
  pages   = {66--73},
  year    = 2018
}

@Article{SmaMcCAll2011efficient,
  title =        {Efficient discovery of anti-inflammatory small-molecule
                  combinations using evolutionary computing},
  author =       {Small, Ben G. and McColl, Barry W.} #and# Allmendinger #and#
                  {Pahle, J{\"u}rgen and L{\'o}pez-Castej{\'o}n, Gloria and
                  Rothwell, Nancy J.} #and# Knowles #and# {Mendes, Pedro and
                  Brough, David and Kell, Douglas B.},
  journal =      naturechembio,
  volume =       7,
  number =       12,
  pages =        {902--908},
  year =         2011,
  publisher =    {Nature Publishing Group}
}

@Article{SmiBaaWreLew2014isa,
  author       = Smith-Miles_K #and# {Baatar, Davaatseren and Wreford, Brendan}
                  #and# Lewis_R,
  title        = {Towards Objective Measures of Algorithm Performance across
                  Instance Space},
  journal      = cor,
  year         = 2014,
  volume       = 45,
  pages        = {12--24},
  doi          = {10.1016/j.cor.2013.11.015},
  abstract     = {This paper tackles the difficult but important task of
                  objective algorithm performance assessment for
                  optimization. Rather than reporting average performance of
                  algorithms across a set of chosen instances, which may bias
                  conclusions, we propose a methodology to enable the strengths
                  and weaknesses of different optimization algorithms to be
                  compared across a broader instance space. The results
                  reported in a recent Computers and Operations Research paper
                  comparing the performance of graph coloring heuristics are
                  revisited with this new methodology to demonstrate (i) how
                  pockets of the instance space can be found where algorithm
                  performance varies significantly from the average performance
                  of an algorithm; (ii) how the properties of the instances can
                  be used to predict algorithm performance on previously unseen
                  instances with high accuracy; and (iii) how the relative
                  strengths and weaknesses of each algorithm can be visualized
                  and measured objectively.},
  keywords     = {Algorithm selection; Instance Space Analysis; Graph coloring;
                  Heuristics; Performance prediction}
}

@Article{SmiBow2015:cor,
  author       = Smith-Miles_K #and# {Simon Bowly},
  title        = {Generating New Test Instances by Evolving in Instance Space},
  journal      = cor,
  year         = 2015,
  volume       = 63,
  pages        = {102--113},
  doi          = {10.1016/j.cor.2015.04.022},
  abstract     = {Our confidence in the future performance of any algorithm,
                  including optimization algorithms, depends on how carefully
                  we select test instances so that the generalization of
                  algorithm performance on future instances can be inferred. In
                  recent work, we have established a methodology to generate a
                  2-d representation of the instance space, comprising a set of
                  known test instances. This instance space shows the
                  similarities and differences between the instances using
                  measurable features or properties, and enables the
                  performance of algorithms to be viewed across the instance
                  space, where generalizations can be inferred. The power of
                  this methodology is the insights that can be generated into
                  algorithm strengths and weaknesses by examining the regions
                  in instance space where strong performance can be
                  expected. The representation of the instance space is
                  dependent on the choice of test instances however. In this
                  paper we present a methodology for generating new test
                  instances with controllable properties, by filling observed
                  gaps in the instance space. This enables the generation of
                  rich new sets of test instances to support better the
                  understanding of algorithm strengths and weaknesses. The
                  methodology is demonstrated on graph colouring as a case
                  study.},
  keywords     = {Benchmarking; Evolving instances; Graph colouring; Instance
                  space; Test instances}
}

@Article{SmiChrMun2021where,
  author       = Smith-Miles_K #and# {Jeffrey Christiansen} #and# Munoz_MA,
  title        = {Revisiting Where Are the Hard Knapsack Problems? Via
                  {Instance} {Space} {Analysis}},
  journal      = cor,
  year         = 2021,
  volume       = 128,
  pages        = 105184,
  doi          = {10.1016/j.cor.2020.105184},
  keywords     = {0-1 Knapsack problem; Algorithm portfolios; Algorithm
                  selection; Instance difficulty; Instance generation; Instance
                  Space Analysis; Performance evaluation}
}

@Article{SmiLop2012:cor,
  author =       Smith-Miles_K #and# {Lopes, Leo},
  title =        {Measuring instance difficulty for combinatorial optimization
                  problems},
  journal =      cor,
  year =         2012,
  volume =       39,
  pages =        {875--889}
}

@Article{SmiMun2023isa,
  author       = Smith-Miles_K #and# Munoz_MA,
  title        = {Instance Space Analysis for Algorithm Testing: Methodology
                  and Software Tools},
  journal      = acm-cs,
  year         = 2023,
  volume       = 55,
  number       = 12,
  month        = mar,
  issue_date   = {December 2023},
  doi          = {10.1145/3572895},
  abstract     = {Instance Space Analysis (ISA) is a recently developed
                  methodology to (a) support objective testing of algorithms
                  and (b) assess the diversity of test instances. Representing
                  test instances as feature vectors, the ISA methodology
                  extends Rice's 1976 Algorithm Selection Problem framework to
                  enable visualization of the entire space of possible test
                  instances, and gain insights into how algorithm performance
                  is affected by instance properties. Rather than reporting
                  algorithm performance on average across a chosen set of test
                  problems, as is standard practice, the ISA methodology offers
                  a more nuanced understanding of the unique strengths and
                  weaknesses of algorithms across different regions of the
                  instance space that may otherwise be hidden on average. It
                  also facilitates objective assessment of any bias in the
                  chosen test instances and provides guidance about the
                  adequacy of benchmark test suites. This article is a
                  comprehensive tutorial on the ISA methodology that has been
                  evolving over several years, and includes details of all
                  algorithms and software tools that are enabling its worldwide
                  adoption in many disciplines. A case study comparing
                  algorithms for university timetabling is presented to
                  illustrate the methodology and tools.},
  articleno    = 255,
  numpages     = 31,
  keywords     = {test instance diversity, benchmarking, timetabling, Algorithm
                  footprints, MATLAB, software as a service, meta-heuristics,
                  algorithm selection, meta-learning}
}

@Article{Smith-Miles2008,
  author = 	 Smith-Miles_K,
  title = 	 {Cross-disciplinary Perspectives on Meta-learning for Algorithm Selection},
  journal = 	 acm-cs,
  year = 	 2008,
  volume = 	 41,
  number = 	 1,
  pages = 	 {1--25}
}

@Article{SocBlu07,
  author =       Socha #and# Blum,
  title =        {An ant colony optimization algorithm for continuous
                  optimization: An application to feed-forward neural
                  network training},
  journal =      nca,
  year =         2007,
  volume =       16,
  number =       3,
  pages =        {235--247},
  alias = {SocBlu2007:nca},
}

@Article{SocDor2008:ejor,
  author =       Socha #and# Dorigo,
  title =        {Ant Colony Optimization for Continuous Domains},
  year =         2008,
  journal =      ejor,
  volume =       185,
  number =       3,
  pages =        {1155--1173},
  doi =          {10.1016/j.ejor.2006.06.046},
  annote =       "Proposed ACOR (ACO$_\mathbb{R}$)",
  keywords =     "ACOR"
}

@Article{Sol2002:tec,
  author =       Solnon,
  title =        "Ants Can Solve Constraint Satisfaction Problems",
  journal =      tec,
  year =         2002,
  volume =       6,
  number =       4,
  pages =        {347--357}
}

@Article{SolMarMic2008,
  author =       {D. Soler and E. Mart{\'i}nez and J. C. Mic\'o},
  title =        {A Transformation for the Mixed General Routing Problem with Turn Penalties},
  journal =      jors,
  year =         2008,
  volume =       59,
  pages =        {540--547}}

@Article{Solomon87vrptw,
  author =       Solomon,
  title =        "Algorithms for the Vehicle Routing and Scheduling
                  Problems with Time Windows",
  journal =      or,
  year =         1987,
  volume =       35,
  pages =        {254--265},
}

@Article{SonWanHeJin2021kriging,
  title =        {A {Kriging}-assisted two-archive evolutionary algorithm for
                  expensive many-objective optimization},
  author =       {Song, Zhenshou and Wang, Handing and He, Cheng} #and#
                  Jin_Yaochu,
  journal =      tec,
  volume =       25,
  number =       6,
  pages =        {1013--1027},
  year =         2021,
  publisher =    {IEEE}
}

@Article{Sor2013,
  title =         "Metaheuristics---the metaphor exposed",
  author =        Sorensen,
  journal =       itor,
  year =          2015,
  volume =        22,
  number =        1,
  pages =        {3--18},
  doi = {10.1111/itor.12001},
}

@Article{SorArnPal2017,
  author =       Sorensen #and# Arnold_F #and# {Daniel {Palhazi Cuervo}},
  title =        {A critical analysis of the ``improved {Clarke} and {Wright}
                  savings algorithm''},
  journal =      itor,
  volume =       26,
  number =       1,
  pages =        {54--63},
  year =         2017,
  alias = "SorArnPal2019",
  doi =          "10.1111/itor.12443",
  keywords =     "reproducibility, vehicle routing",
}

@Article{SorOchSotBur2017:ejor,
  author = 	 {Jorge A. Soria-Alcaraz} #and# Ochoa #and# {Marco A. Sotelo-Figeroa} #and# Burke_E,
  title = 	 {A Methodology for Determining an Effective Subset of Heuristics in Selection Hyper-heuristics},
  journal = 	 ejor,
  year = 	 2017,
  volume = 	 260,
  pages = 	 {972--983}
}

@Article{SouRitLop2021cap,
  author =       DeSouza #and# Ritt #and# Lopez-Ibanez,
  title =        {Capping Methods for the Automatic Configuration of
                  Optimization Algorithms},
  journal =      cor,
  doi =          "10.1016/j.cor.2021.105615",
  year =         2022,
  volume =       139,
  pages =        105615,
  supplement =   "https://github.com/souzamarcelo/supp-cor-capopt",
  abstract =     "Automatic configuration techniques are widely and
                  successfully used to find good parameter settings for
                  optimization algorithms. Configuration is costly, because it
                  is necessary to evaluate many configurations on different
                  instances. For decision problems, when the objective is to
                  minimize the running time of the algorithm, many
                  configurators implement capping methods to discard poor
                  configurations early. Such methods are not directly
                  applicable to optimization problems, when the objective is to
                  optimize the cost of the best solution found, given a
                  predefined running time limit. We propose new capping methods
                  for the automatic configuration of optimization
                  algorithms. They use the previous executions to determine a
                  performance envelope, which is used to evaluate new
                  executions and cap those that do not satisfy the envelope
                  conditions. We integrate the capping methods into the irace
                  configurator and evaluate them on different optimization
                  scenarios. Our results show that the proposed methods can
                  save from about 5\% to 78\% of the configuration effort,
                  while finding configurations of the same quality. Based on
                  the computational analysis, we identify two conservative and
                  two aggressive methods, that save an average of about 20\%
                  and 45\% of the configuration effort, respectively. We also
                  provide evidence that capping can help to better use the
                  available budget in scenarios with a configuration time
                  limit."
}

@Article{Souilah1995,
  title =         {Simulated annealing for manufacturing systems layout design},
  author =        {Souilah, Abdelghani},
  journal =       ejor,
  volume =        82,
  number =        3,
  pages =         {592--614},
  year =          1995,
  publisher =     elsevier
}

@Article{Spearman1904,
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  journal   = ajs,
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  number    = 1,
  pages     = {72--101},
  year      = 1904,
  publisher = uip
}

@Article{SpecCPU2000,
  author    =  {J. L. Henning},
  journal   =  computer,
  title     =  {{SPEC} {CPU2000}: measuring {CPU} performance in the New Millennium},
  publisher =  ieee-csp,
  year      =  2000,
  volume    =  33,
  number    =  7,
  pages     =  {28--35},
  doi       =  {10.1109/2.869367}
}

@Article{SpiTen2004,
  title =        {Smoothed analysis of algorithms: Why the simplex algorithm
                  usually takes polynomial time},
  author =       {Spielman, Daniel A. and Teng, Shang-Hua},
  journal =      jacm,
  volume =       51,
  number =       3,
  pages =        {385--463},
  year =         2004,
  publisher =    acm-pub
}

@Article{SprHarDre1997ors,
  author =       {Sprecher, Arno and Hartmann, S{\"o}nke and Drexl, Andreas},
  title =        {An exact algorithm for project scheduling with
                  multiple modes},
  volume =       19,
  doi =          {10.1007/BF01545587},
  number =       3,
  journal =      orspek,
  year =         1997,
  keywords =     {branch-and-bound, multi-mode resource-constrained
                  project scheduling, project scheduling},
  pages =        {195--203},
}

@Article{SprKolDre1995ejor,
  author =       {Sprecher, Arno and Kolisch, Rainer and Drexl,
                  Andreas},
  title =        {Semi-active, active, and non-delay schedules for the
                  resource-constrained project scheduling problem},
  volume =       80,
  doi =          {10.1016/0377-2217(93)E0294-8},
  abstract =     {We consider the resource-constrained project
                  scheduling problem ({RCPSP).} The focus of the paper
                  is on a formal definition of semi-active, active,
                  and non-delay schedules. Traditionally these
                  schedules establish basic concepts within the job
                  shop scheduling literature. There they are usually
                  defined in a rather informal way which does not
                  create any substantial problems. Using these
                  concepts in the more general {RCPSP} without giving
                  a formal definition may cause serious
                  problems. After providing a formal definition of
                  semi-active, active, and non-delay schedules for the
                  {RCPSP} we outline some of these problems occurring
                  within the disjunctive arc concept.},
  number =       1,
  journal =      ejor,
  year =         1995,
  keywords =     {active schedules, Branch-and-bound methods,
                  non-delay schedules, Resource-constrained project
                  scheduling, Semi-active schedules},
  pages =        {94--102},
}

@Article{SriDeb1994:nsga,
  author =       {N. Srinivas} #and# Deb,
  title =        {Multiobjective Optimization Using Nondominated
                  Sorting in Genetic Algorithms},
  journal =      ec,
  year =         1994,
  volume =       2,
  pages =        {221--248},
  number =       3
}

@Article{Ste1996jmcda,
  author =       Stewart_TJ,
  title =        "Robustness of Additive Value Function Methods in {MCDM}",
  journal =      jmcda,
  year =         1996,
  volume =       5,
  number =       4,
  pages =        {301--309},
  keywords = {machine decision-making}
}

@Article{Ste1999ejor,
  author =       Stewart_TJ,
  title =        "Evaluation and refinement of aspiration-based methods in {MCDM}",
  journal =      ejor,
  year =         1999,
  volume =       113,
  number =       3,
  pages =        {643--652},
  keywords = {machine decision-making}
}

@Article{Ste2005jors,
  author =       Stewart_TJ,
  title =        {Goal programming and cognitive biases in decision-making},
  volume =       56,
  doi =          {10.1057/palgrave.jors.2601948},
  number =       10,
  journal =      jors,
  year =         2005,
  keywords =     {machine decision making},
  pages =        {1166--1175},
}

@Article{SteFreRio2013omg,
  title =        {Integrating multicriteria decision analysis and scenario
                  planning: Review and extension},
  author =       Stewart_TJ #and# {Simon French and Jesus Rios},
  journal =      omega,
  year =         2013,
  number =       4,
  pages =        {679--688},
  volume =       41,
  doi =          {10.1016/j.omega.2012.09.003},
  keywords =     {Multicriteria decision analysis},
}

@Article{SteHeiHah2020,
  author =       {Stegherr, Helena and Heider, Michael and H\"ahner, J\"org},
  title =        {Classifying Metaheuristics: Towards a unified multi-level classification system},
  journal =      natcomp,
  year =         2020,
  doi =          "10.1007/s11047-020-09824-0"
}

@Article{SteRad2008computing,
  title =        {Computing all efficient solutions of the biobjective minimum
                  spanning tree problem},
  author =       {Steiner, Sarah and Radzik, Tomasz},
  journal =      cor,
  year =         2008,
  number =       1,
  pages =        {198--211},
  volume =       35,
}

@Article{Sto2014edge,
  author =       {Stodden, Victoria},
  title =        {What scientific idea is ready for retirement?
                  Reproducibility},
  journal =      {Edge},
  year =         2014,
  url =          "https://www.edge.org/annual-question/2014/response/25340",
  annote =       "Introduces computational reproducibility, empirical
                  reproducibility and statistical reproducibility"
}

@Article{StoAlb2014,
  author =       {Stolfi, Daniel H.} #and# Alba_E,
  doi =          {10.1016/j.asoc.2014.07.014},
  journal =      asoco,
  keywords =     {Evolutionary algorithm,Road traffic,Smart city,Smart
                  mobility,Traffic light,WiFi connections},
  pages =        {181--195},
  title =        {Red Swarm: Reducing travel times in smart cities by using
                  bio-inspired algorithms},
  volume =       24,
  year =         2014,
  abstract =     "This article presents an innovative approach to solve one of
                  the most relevant problems related to smart mobility: the
                  reduction of vehicles' travel time. Our original approach,
                  called Red Swarm, suggests a potentially customized route to
                  each vehicle by using several spots located at traffic lights
                  in order to avoid traffic jams by using \{V2I\}
                  communications. That is quite different from other existing
                  proposals, as it deals with real maps and actual streets, as
                  well as several road traffic distributions. We propose an
                  evolutionary algorithm (later efficiently parallelized) to
                  optimize our case studies which have been imported from
                  OpenStreetMap into \{SUMO\} as they belong to a real city. We
                  have also developed a Rerouting Algorithm which accesses the
                  configuration of the Red Swarm and communicates the route
                  chosen to vehicles, using the spots (via WiFi
                  link). Moreover, we have developed three competing algorithms
                  in order to compare their results to those of Red Swarm and
                  have observed that Red Swarm not only achieved the best
                  results, but also outperformed the experts' solutions in a
                  total of 60 scenarios tested, with up to 19\% shorter travel
                  times."
}

@Article{StoMcNutBai2016enhancing,
  doi =          {10.1126/science.aah6168},
  year =         2016,
  month =        dec,
  publisher =    {American Association for the Advancement of Science ({AAAS})},
  volume =       354,
  number =       6317,
  pages =        {1240--1241},
  author =       {Stodden, Victoria and Marcia McNutt and David H. Bailey and Ewa Deelman and
                  Yolanda Gil and Brooks Hanson and Michael A. Heroux} #and# Ioannidis #and# {Michela Taufer},
  title =        {Enhancing reproducibility for computational methods},
  journal =      Science
}

@Article{StoPri1997:de,
  author       = {Storn, Rainer and Price, Kenneth},
  title        = {Differential Evolution -- A Simple and Efficient Heuristic
                  for Global Optimization over Continuous Spaces},
  journal      = jgo,
  year         = 1997,
  volume       = 11,
  number       = 4,
  pages        = {341--359},
  annote       = {Proposed differential evolution},
  doi          = {10.1023/A:1008202821328}
}

@Article{StoSeiZha2018pnas,
  author =       {Stodden, Victoria and Jennifer Seiler and Zhaokun Ma},
  title =        {An empirical analysis of journal policy effectiveness for
                  computational reproducibility},
  doi =          {10.1073/pnas.1708290115},
  year =         2018,
  month =        mar,
  journal =      pnas,
  volume =       115,
  number =       11,
  pages =        {2584--2589},
}

@Article{StrKir1991,
  title =        {Analysis of Finite Length Annealing Schedules},
  author =       Strenski_PN #and# Kirkpatrick,
  journal =      alg,
  volume =       6,
  number =       {1-6},
  pages =        {346--366},
  year =         1991,
  publisher =    springer
}

@Article{StrLopBroLee2020,
  title        = "General Northern English: Exploring regional variation in the
                  North of England with machine learning",
  author       = Strycharczuk #and# Lopez-Ibanez #and# Brown_G #and# Leemann_A,
  journal      = front-ai,
  year         = 2020,
  volume       = 3,
  number       = 48,
  keywords     = "vowels, accent features, dialect leveling, Random forest
                  (bagging), Feature selecion",
  doi          = "10.3389/frai.2020.00048",
  abstract     = "In this paper, we present a novel computational approach to
                  the analysis of accent variation. The case study is dialect
                  leveling in the North of England, manifested as reduction of
                  accent variation across the North and emergence of General
                  Northern English (GNE), a pan-regional standard accent
                  associated with middle-class speakers. We investigated this
                  instance of dialect leveling using random forest
                  classification, with audio data from a crowd-sourced corpus
                  of 105 urban, mostly highly-educated speakers from five
                  northern UK cities: Leeds, Liverpool, Manchester, Newcastle
                  upon Tyne, and Sheffield. We trained random forest models to
                  identify individual northern cities from a sample of other
                  northern accents, based on first two formant measurements of
                  full vowel systems. We tested the models using unseen
                  data. We relied on undersampling, bagging (bootstrap
                  aggregation) and leave-one-out cross-validation to address
                  some challenges associated with the data set, such as
                  unbalanced data and relatively small sample size. The
                  accuracy of classification provides us with a measure of
                  relative similarity between different pairs of cities, while
                  calculating conditional feature importance allows us to
                  identify which input features (which vowels and which
                  formants) have the largest influence in the prediction. We do
                  find a considerable degree of leveling, especially between
                  Manchester, Leeds and Sheffield, although some differences
                  persist. The features that contribute to these differences
                  most systematically are typically not the ones discussed in
                  previous dialect descriptions. We propose that the most
                  systematic regional features are also not salient, and as
                  such, they serve as sociolinguistic regional indicators. We
                  supplement the random forest results with a more traditional
                  variationist description of by-city vowel systems, and we use
                  both sources of evidence to inform a description of the
                  vowels of General Northern English."
}

@Article{Stu06:ejor,
  author =       Stuetzle,
  title =        {Iterated Local Search for the Quadratic Assignment Problem},
  journal =      ejor,
  year =         2006,
  volume =       174,
  number =       3,
  pages =        {1519--1539}
}

@Article{StuDor2002:tec,
  author =       Stuetzle #and# Dorigo,
  title =        "A Short Convergence Proof for a Class of {ACO}
                  Algorithms",
  volume =       6,
  number =       4,
  pages =        "358--365",
  journal =      tec,
  year =         2002,
}

@Article{StuHoo2000:fgcs,
  author =       Stuetzle #and# Hoos,
  title =        "{\MaxMinAntSystem}",
  journal =      fgcs,
  year =         2000,
  volume =       16,
  number =       8,
  pages =        {889--914},
  alias = {StuHoo00:fgcs},
}

@Article{SuZhaYue2021enhanced,
  title =        {Enhanced Constraint Handling for Reliability-Constrained
                  Multiobjective Testing Resource Allocation},
  author =       {Su, Zhaopin and Zhang, Guofu and Yue, Feng and Zhan, Dezhi}
                  #and# Li_Miqing #and# {Li, Bin} #and# Yao_Xin,
  journal =      tec,
  year =         2021,
  number =       3,
  pages =        {537--551},
  volume =       25,
  publisher =    {IEEE}
}

@Article{SubBat2013,
  author =       Subramanian #and# {Maria Battarra},
  title =        {An Iterated Local Search Algorithm for the Travelling Salesman Problem with Pickups and Deliveries},
  journal =      jors,
  year =         2013,
  volume =       64,
  number =       3,
  pages =        {402--409}}

@Article{SubBatPot2014,
  author =       Subramanian #and# {Maria Battarra} #and# Potts,
  title =        {An Iterated Local Search Heuristic for the Single Machine Total Weighted Tardiness Scheduling Problem with Sequence-dependent Setup Times},
  journal =      ijpr,
  year =         2014,
  volume =       52,
  number =       9,
  pages =        {2729--2742}
}

@Article{SuiZhuBur2018arxiv,
  author       = {Sui, Yanan and Zhuang, Vincent and Burdick, Joel W. and Yue,
                  Yisong},
  title        = {Stagewise Safe {Bayesian} Optimization with {Gaussian}
                  Processes},
  journal      = arxiv # {1806.07555},
  year         = 2018,
  note         = {Published as \cite{SuiZhuBur2018stageopt}},
  url          = {https://arxiv.org/abs/1806.07555},
  abstract     = {Enforcing safety is a key aspect of many problems pertaining
                  to sequential decision making under uncertainty, which
                  require the decisions made at every step to be both
                  informative of the optimal decision and also safe. For
                  example, we value both efficacy and comfort in medical
                  therapy, and efficiency and safety in robotic control. We
                  consider this problem of optimizing an unknown utility
                  function with absolute feedback or preference feedback
                  subject to unknown safety constraints. We develop an
                  efficient safe Bayesian optimization algorithm, StageOpt,
                  that separates safe region expansion and utility function
                  maximization into two distinct stages. Compared to existing
                  approaches which interleave between expansion and
                  optimization, we show that StageOpt is more efficient and
                  naturally applicable to a broader class of problems. We
                  provide theoretical guarantees for both the satisfaction of
                  safety constraints as well as convergence to the optimal
                  utility value. We evaluate StageOpt on both a variety of
                  synthetic experiments, as well as in clinical practice. We
                  demonstrate that StageOpt is more effective than existing
                  safe optimization approaches, and is able to safely and
                  effectively optimize spinal cord stimulation therapy in our
                  clinical experiments.},
  keywords     = {Safe Optimization, StageOpt}
}

@Article{SunYenYi2019igd,
  title =        {{IGD} Indicator-based Evolutionary Algorithm for
                  Many-objective Optimization Problems},
  author =       {Sun, Yanan and Yen, Gary G. and Yi, Zhang},
  journal =      tec,
  publisher =    {IEEE},
  volume =       23,
  number =       2,
  pages =        {173--187},
  year =         2019,
  doi =          {10.1109/TEVC.2018.2791283}
}

@Article{SupSefPar2000,
  title =        {A simulated annealing algorithm for multiobjective optimization},
  author =       {Suppapitnarm, A. and Seffen, K. A. and Parks, G. T. and Clarkson, P. J.},
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  volume =       33,
  number =       1,
  pages =        {59--85},
  year =         2000,
}

@Article{SuyVan1999least,
  title =        {Least Squares Support Vector Machine Classifiers},
  author =       {Suykens, Johan A. K. and Vandewalle, Joos},
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  volume =       9,
  number =       3,
  pages =        {293--300},
  year =         1999,
  publisher =    {Springer},
  doi =          {10.1023/A:1018628609742},
  keywords =     "LS-SVM"
}

@Article{SwaAdrBar2019extending,
  author       = Swan_J #and# Adriaensen_S #and# {Barwell, Adam D. and Hammond,
                  Kevin and White, David R.},
  title        = {Extending the ``Open-Closed Principle'' to Automated
                  Algorithm Configuration},
  journal      = ec,
  year         = 2019,
  volume       = 27,
  number       = 1,
  pages        = {173--193},
  doi          = {10.1162/evco_a_00245}
}

@Article{SwaAdrBro2022metah,
  author =       Swan_J #and# Adriaensen_S #and# "Alexander
                  E. I. Brownlee" #and# "Kevin Hammond" #and# "Colin
                  G. Johnson" #and# "Ahmed Kheiri" #and# "Faustyna Krawiec"
                  #and# Merelo_JJ #and# "Leandro L. Minku" #and# Ozcan_E #and#
                  Pappa_G #and# Garcia-Sanchez_P #and# Sorensen #and# Voss_S
                  #and# Wagner_M #and# "David R. White",
  doi =          {10.1016/j.ejor.2021.05.042},
  year =         2022,
  month =        mar,
  volume =       297,
  number =       2,
  pages =        {393--406},
  title =        {Metaheuristics ``In the Large''},
  journal =      ejor,
}

@Article{SwaWooOzc2014cogcomp,
  author =       Swan_J #and# Woodward_J #and# Ozcan_E #and#
                  Kendall_G #and# Burke_E,
  title =        {Searching the Hyper-heuristic Design Space},
  journal =      cogcomp,
  year =         2014,
  volume =       6,
  number =       1,
  pages =        {66--73},
  month =        mar,
  doi =          {10.1007/s12559-013-9201-8},
}

@Article{SzuHar1987,
  title =        {Fast Simulated Annealing},
  author =       Szu_H #and# Hartley_R,
  journal =      pla,
  volume =       122,
  number =       3,
  pages =        {157--162},
  year =         1987,
  publisher =    elsevier
}

@Article{Tai90,
  author =       Taillard,
  title =        "Some Efficient Heuristic Methods for the Flow Shop
                  Sequencing Problem",
  journal =      ejor,
  year =         1990,
  volume =       47,
  number =       1,
  pages =        {65--74}
}

@Article{Tai91,
  author =       Taillard,
  title =        {Robust Taboo Search for the Quadratic Assignment Problem},
  journal =      pc,
  year =         1991,
  volume =       17,
  number =       {4-5},
  pages =        {443--455},
  annote =       {faster 2-exchange delta evaluation in QAP}
}

@Article{Tai93,
  author =       Taillard,
  title =        "Benchmarks for Basic Scheduling Problems",
  journal =      ejor,
  year =         1993,
  volume =       64,
  number =       2,
  pages =        {278--285}
}

@Article{Tai95,
  author =       Taillard,
  title =        {Comparison of Iterative Searches for the Quadratic
                  Assignment Problem},
  journal =      ls,
  year =         1995,
  volume =       3,
  number =       2,
  pages =        {87--105}
}

@Article{Talbi02,
  author =       Talbi,
  title =        {A Taxonomy of Hybrid Metaheuristics},
  journal =      joh,
  year =         2002,
  volume =       8,
  number =       5,
  pages =        {541--564}
}

@Article{Tam1992,
  title =        {A Simulated Annealing Algorithm for Allocating Space to Manufacturing Cells},
  author =       Tam_KY,
  journal =      ijpr,
  volume =       30,
  number =       1,
  pages =        {63--87},
  year =         1992,
  publisher =    taylorfrancis
}

@Article{TamJonEld1995,
  author       = {Tamiz, M. and Jones, D. F. and El-Darzi, E.},
  title        = {A review of {Goal} {Programming} and its applications},
  journal      = aor,
  year         = 1995,
  volume       = 58,
  number       = 1,
  pages        = {39--53},
  month        = jan,
  doi          = {10.1007/BF02032309},
  abstract     = {This paper presents a review of the current literature on the
                  branch of multi-criteria decision modelling known as Goal
                  Programming (GP). The result of our indepth investigations of
                  the two main GP methods, lexicographic and weighted GP
                  together with their distinct application areas is
                  reported. Some guidelines to the scope of GP as an
                  application tool are given and methods of determining which
                  problem areas are best suited to the different GP approaches
                  are proposed. The correlation between the method of assigning
                  weights and priorities and the standard of the results is
                  also ascertained.},
  language     = {en},
  keywords     = {Goal Programming, lexicographic, weighted}
}

@Article{TanAra2013,
  author = 	 {Shunji Tanaka and Mituhiko Araki},
  title = 	 {An Exact Algorithm for the Single-machine Total Weighted Tardiness
               Problem with Sequence-dependent Setup Times},
  journal = 	 cor,
  year = 	 2013,
  volume = 	 40,
  number = 	 1,
  pages = 	 {344--352}
}

@Article{TanIsh2020re,
  author =       {Ryoji Tanabe} #and# Ishibuchi,
  title =        {An easy-to-use real-world multi-objective optimization
                  problem suite},
  journal =      asoco,
  volume =       89,
  pages =        106078,
  year =         2020,
  annote =       "Proposed the RE benchmark suite"
}

@Article{TanIshOya2017,
  author =       {Ryoji Tanabe} #and# Ishibuchi #and# {Akira Oyama},
  journal =      ieee-access,
  title =        {Benchmarking Multi- and Many-Objective Evolutionary
                  Algorithms Under Two Optimization Scenarios},
  year =         2017,
  volume =       5,
  pages =        {19597--19619},
  annote =       "compared a number of MOEAs using a wide range of numbers of
                  objectives and stopping criteria, with and without archivers; unbounded archive"
}

@Article{TanWan2006,
  author =       {Lixin Tang and Xianpeng Wang},
  title =        {Iterated local search algorithm based on very large-scale
                  neighborhood for prize-collecting vehicle routing problem},
  journal =      ijamt,
  year =         2006,
  volume =       29,
  number =       11,
  pages =        {1246--1258},
}

@Article{Tarquin89,
  author =       Tarquin #and# Dowdy,
  title =        {Optimal pump operation in water distribution},
  journal =      jhe,
  year =         1989,
  volume =       115,
  number =       2,
  pages =        {158--169 or 496--501},
  month =        feb,
  note =         notreadyet,
}

@Article{TasKizPanSug2017,
  author       = {M. F. Tasgetiren and D. Kizilay} #and# Pan_QK #and# Suganthan,
  title        = {Iterated Greedy Algorithms for the Blocking Flowshop
                  Scheduling Problem with Makespan Criterion},
  journal      = cor,
  year         = 2017,
  volume       = 77,
  pages        = {111--126}
}

@Article{TasLiaSevGen2007,
  author       = {M. Fatih Tasgetiren and Yun-Chia Liang and Mehmet Sevkli and
                  Gunes Gencyilmaz},
  title        = {A particle swarm optimization algorithm for makespan and
                  total flowtime minimization in the permutation flowshop
                  sequencing problem},
  journal      = ejor,
  year         = 2007,
  volume       = 177,
  number       = 3,
  pages        = {1930--1947},
  doi          = {10.1016/j.ejor.2005.12.024}
}

@Article{TasPanSugBuy2013:cor,
  author       = {M. Fatih Tasgetiren} #and# Pan_QK #and# Suganthan #and# {Ozge
                  Buyukdagli},
  title        = {A variable iterated greedy algorithm with differential
                  evolution for the no-idle permutation flowshop scheduling
                  problem},
  journal      = cor,
  year         = 2013,
  volume       = 40,
  number       = 7,
  pages        = {1729--1743}
}

@Article{TayHo2008cie,
  author =       {Joc Cing Tay and Nhu Binh Ho},
  title =        "Evolving dispatching rules using genetic programming for
                  solving multi-objective flexible job-shop problems",
  journal =      cie,
  volume =       54,
  number =       3,
  pages =        {453 -- 473},
  year =         2008,
  doi =          "10.1016/j.cie.2007.08.008"
}

@Article{TeiCovStuCun11itor,
  author =       {Cristina Teixeira} #and# {Jos{\'e} Covas} #and# Stuetzle
                  #and# Gaspar-Cunha,
  title =        {Engineering an Efficient Two-Phase Local Search for the
                  Co-Rotating Twin-Screw Configuration Problem},
  journal =      itor,
  year =         2011,
  volume =       18,
  number =       2,
  pages =        {271--291}
}

@Article{TeiCovStuCun12eo,
  author =       {Cristina Teixeira} #and# {Jos{\'e} Covas} #and# Stuetzle #and# Gaspar-Cunha,
  title =        {Multi-Objective Ant Colony Optimization for Solving the
Twin-Screw Extrusion Configuration Problem},
  journal =      eo,
  year =         2012,
  volume =       44,
  number =       3,
  pages =        {351--371}
}

@Article{TeiCovStuCun14asc,
  author =       {Cristina Teixeira} #and# {Jos{\'e} Covas} #and# Stuetzle #and# Gaspar-Cunha,
  title =        {Hybrid Algorithms for the Twin-Screw Extrusion Configuration
  Problem},
  journal =      asoco,
  year =         2014,
  volume =       23,
  pages =        {298--307}
}

@Article{Teklu2007,
  author =       {Teklu, Fitsum and Sumalee, Agachai and Watling, David},
  doi =          {10.1111/j.1467-8667.2006.00468.x},
  journal =      cacie,
  month =        jan,
  number =       1,
  pages =        {31--43},
  title =        {A Genetic Algorithm Approach for Optimizing Traffic Control
                  Signals Considering Routing},
  volume =       22,
  year =         2007
}

@Article{TenSilLan2000,
  author =       {J. B. Tenenbaum and V. D. Silva and J. C. Langford},
  title =        {A global geometric framework for nonlinear dimensionality
                  reduction},
  journal =      Science,
  volume =       290,
  number =        5500,
  year =         2000,
  pages =        {2319--2323}
}

@Article{TeoAbb2004ec,
  author =       {J. Teo} #and# Abbass,
  title =        {Automatic generation of controllers for embodied
                  legged organisms: A {Pareto} evolutionary
                  multi-objective approach},
  journal =      ec,
  year =         2004,
  volume =       12,
  number =       3,
  pages =        {355--394},
  doi =          {10.1162/1063656041774974},
  alias =        "Teo:2004vg"
}

@Article{TerSumTam2021auto,
  title =        {Black-Box Optimization for Automated Discovery},
  author =       {Terayama, Kei and Sumita, Masato and Tamura, Ryo and Tsuda,
                  Koji},
  year =         2021,
  month =        mar,
  journal =      achemres,
  volume =       54,
  number =       6,
  pages =        {1334--1346},
  publisher =    {American Chemical Society},
  doi =          {10.1021/acs.accounts.0c00713},
  abstract =     {In chemistry and materials science, researchers and engineers
                  discover, design, and optimize chemical compounds or
                  materials with their professional knowledge and
                  techniques. At the highest level of abstraction, this process
                  is formulated as black-box optimization. For instance, the
                  trial-and-error process of synthesizing various molecules for
                  better material properties can be regarded as optimizing a
                  black-box function describing the relation between a chemical
                  formula and its properties. Various black-box optimization
                  algorithms have been developed in the machine learning and
                  statistics communities. Recently, a number of researchers
                  have reported successful applications of such algorithms to
                  chemistry. They include the design of photofunctional
                  molecules and medical drugs, optimization of thermal emission
                  materials and high Li-ion conductive solid electrolytes, and
                  discovery of a new phase in inorganic thin films for solar
                  cells.There are a wide variety of algorithms available for
                  black-box optimization, such as Bayesian optimization,
                  reinforcement learning, and active learning. Practitioners
                  need to select an appropriate algorithm or, in some cases,
                  develop novel algorithms to meet their demands. It is also
                  necessary to determine how to best combine machine learning
                  techniques with quantum mechanics- and molecular
                  mechanics-based simulations, and experiments. In this
                  Account, we give an overview of recent studies regarding
                  automated discovery, design, and optimization based on
                  black-box optimization. The Account covers the following
                  algorithms: Bayesian optimization to optimize the chemical or
                  physical properties, an optimization method using a quantum
                  annealer, best-arm identification, gray-box optimization, and
                  reinforcement learning. In addition, we introduce active
                  learning and boundless objective-free exploration, which may
                  not fall into the category of black-box optimization.Data
                  quality and quantity are key for the success of these
                  automated discovery techniques. As laboratory automation and
                  robotics are put forward, automated discovery algorithms
                  would be able to match human performance at least in some
                  domains in the near future.}
}

@Article{Thi2011cw,
  author =       {Patrick Thibodeau},
  title =        {Machine-based decision-making is coming},
  journal =      {Computer World},
  year =         2011,
  month =        nov,
  url = {http://www.computerworld.com/s/article/359630/Machine_Based_Decision_Making_Is_Coming},
  note =      {Last accessed: 15 January 2014},
}

@Article{ThiMieKorMol2009ec,
  author =       Thiele #and# Miettinen #and# Korhonen_P #and#
                  Molina_J,
  title =        {A Preference-Based Evolutionary Algorithm for Multi-Objective
                  Optimization},
  journal =      ec,
  volume =       17,
  number =       3,
  pages =        {411--436},
  year =         2009,
  doi =          {10.1162/evco.2009.17.3.411},
  abstract =     { Abstract In this paper, we discuss the idea of incorporating
                  preference information into evolutionary multi-objective
                  optimization and propose a preference-based evolutionary
                  approach that can be used as an integral part of an
                  interactive algorithm. One algorithm is proposed in the
                  paper. At each iteration, the decision maker is asked to give
                  preference information in terms of his or her reference point
                  consisting of desirable aspiration levels for objective
                  functions. The information is used in an evolutionary
                  algorithm to generate a new population by combining the
                  fitness function and an achievement scalarizing function. In
                  multi-objective optimization, achievement scalarizing
                  functions are widely used to project a given reference point
                  into the Pareto optimal set. In our approach, the next
                  population is thus more concentrated in the area where more
                  preferred alternatives are assumed to lie and the whole
                  Pareto optimal set does not have to be generated with equal
                  accuracy. The approach is demonstrated by numerical
                  examples. }
}

@Article{TiaCheZha2017indicator,
  title =        {An Indicator-Based Multiobjective Evolutionary Algorithm With
                  Reference Point Adaptation for Better Versatility},
  author =       {Ye Tian and Ran Cheng and Xingyi Zhang and Fan Cheng} #and# Jin_Yaochu,
  journal =      tec,
  year =         2018,
  volume =       22,
  number =       4,
  pages =        {609--622},
  doi =          {10.1109/TEVC.2017.2749619},
  annote= "IGD-based archiver"
}

@Article{TinRaoLoo2003joh,
  author =       Ting #and# Rao_MVC #and# Loo #and# Ngu,
  title =        {Solving Unit Commitment Problem Using Hybrid
                  Particle Swarm Optimization},
  journal =      joh,
  year =         2003,
  volume =       9,
  number =       6,
  pages =        {507--520},
  doi =          {10.1023/B:HEUR.0000012449.84567.1a},
}

@Article{TiwFadDeb2011amga2,
  title =        {{AMGA2}: Improving the performance of the archive-based
                  micro-genetic algorithm for multi-objective optimization},
  author =       {Tiwari, Santosh and Fadel, Georges} #and# Deb,
  journal =      eo,
  year =         2011,
  number =       4,
  pages =        {377--401},
  volume =       43,
}

@Article{TkiMonTer2002ejor,
  author =       Tkindt #and# Monmarche_N #and# "F. Tercinet" #and#
                  "D. La{\"u}gt",
  title =        {An ant colony optimization algorithm to solve a
                  2-machine bicriteria flowshop scheduling problem},
  journal =      ejor,
  year =         2002,
  volume =       142,
  number =       2,
  pages =        {250--257},
}

@Article{TomKad2019decomposition,
  title =        {Decomposition-based interactive evolutionary algorithm for
                  multiple objective optimization},
  author =       Tomczyk #and# Kadzinski,
  journal =      tec,
  volume =       24,
  number =       2,
  pages =        {320--334},
  year =         2019,
  publisher =    {IEEE},
  doi =          "10.1109/TEVC.2019.2915767",
  abstract =     "We propose a decomposition-based interactive evolutionary
                  algorithm (EA) for multiple objective optimization. During an
                  evolutionary search, a decision maker (DM) is asked to
                  compare pairwise solutions from the current population. Using
                  the Monte Carlo simulation, the proposed algorithm generates
                  from a uniform distribution a set of instances of the
                  preference model compatible with such an indirect preference
                  information. These instances are incorporated as the search
                  directions with the aim of systematically converging a
                  population toward the DMs most preferred region of the Pareto
                  front. The experimental comparison proves that the proposed
                  decomposition-based method outperforms the state-of-the-art
                  interactive counterparts of the dominance-based EAs. We also
                  show that the quality of constructed solutions is highly
                  affected by the form of the incorporated preference model.",
  keywords =     "interactive multi-objective; decision-making"
}

@Article{TomKad2019emosor,
  author =       Tomczyk #and# Kadzinski,
  title =        {{EMOSOR}: Evolutionary multiple objective optimization guided
                  by interactive stochastic ordinal regression},
  journal =      cor,
  volume =       108,
  pages =        {134--154},
  year =         2019,
  doi =          {10.1016/j.cor.2019.04.008},
  keywords =     {Multiple objective optimization, Interactive evolutionary
                  hybrids, Stochastic ordinal regression, Preference
                  disaggregation, Pairwise comparisons, Active learning},
  abstract =     {We propose a family of algorithms, called EMOSOR, combining
                  Evolutionary Multiple Objective Optimization with Stochastic
                  Ordinal Regression. The proposed methods ask the Decision
                  Maker (DM) to holistically compare, at regular intervals, a
                  pair of solutions, and use the Monte Carlo simulation to
                  construct a set of preference model instances compatible with
                  such indirect and incomplete information. The specific
                  variants of EMOSOR are distinguished by the following three
                  aspects. Firstly, they make use of two different preference
                  models, i.e., either an additive value function or a
                  Chebyshev function. Secondly, they aggregate the
                  acceptability indices derived from the stochastic analysis in
                  various ways, and use thus constructed indicators or
                  relations to sort the solutions obtained in each
                  generation. Thirdly, they incorporate different active
                  learning strategies for selecting pairs of solutions to be
                  critically judged by the DM. The extensive computational
                  experiments performed on a set of benchmark optimization
                  problems reveal that EMOSOR is able to bias an evolutionary
                  search towards a part of the Pareto front being the most
                  relevant to the DM, outperforming in this regard the
                  state-of-the-art interactive evolutionary hybrids. Moreover,
                  we demonstrate that the performance of EMOSOR improves in
                  case the forms of a preference model used by the method and
                  the DM's value system align. Furthermore, we discuss how
                  vastly incorporation of different indicators based on the
                  stochastic acceptability indices influences the quality of
                  both the best constructed solution and an entire
                  population. Finally, we demonstrate that our novel
                  questioning strategies allow to reduce a number of
                  interactions with the DM until a high-quality solution is
                  constructed or, alternatively, to discover a better solution
                  after the same number of interactions.}
}

@Article{TomKad2021ciemod,
  author =       Tomczyk #and# Kadzinski,
  title =        {Decomposition-based co-evolutionary algorithm for interactive
                  multiple objective optimization},
  journal =      is,
  volume =       549,
  pages =        {178--199},
  year =         2021,
  doi =          {10.1016/j.ins.2020.11.030},
  keywords =     {Evolutionary multiple objective optimization, Co-evolution,
                  Decomposition, Indirect preference information, Preference
                  learning},
  abstract =     {We propose a novel co-evolutionary algorithm for interactive
                  multiple objective optimization, named CIEMO/D. It aims at
                  finding a region in the Pareto front that is highly relevant
                  to the Decision Maker (DM). For this reason, CIEMO/D asks the
                  DM, at regular intervals, to compare pairs of solutions from
                  the current population and uses such preference information
                  to bias the evolutionary search. Unlike the existing
                  interactive evolutionary algorithms dealing with just a
                  single population, CIEMO/D co-evolves a pool of
                  subpopulations in a steady-state decomposition-based
                  evolutionary framework. The evolution of each subpopulation
                  is driven by the use of a different preference model. In this
                  way, the algorithm explores various regions in the objective
                  space, thus increasing the chances of finding DM's most
                  preferred solution. To improve the pace of the evolutionary
                  search, CIEMO/D allows for the migration of solutions between
                  different subpopulations. It also dynamically alters the
                  subpopulations' size based on compatibility between the
                  incorporated preference models and the decision examples
                  supplied by the DM. The extensive experimental evaluation
                  reveals that CIEMO/D can successfully adjust to different
                  DM's decision policies. We also compare CIEMO/D with selected
                  state-of-the-art interactive evolutionary hybrids that make
                  use of the DM's pairwise comparisons, demonstrating its high
                  competitiveness.}
}

@Article{TorRosKefetal2010:si,
  author =       {C. E. Torres and L. F. Rossi and J. Keffer and K. Li and
                  C.-C. Shen},
  title =        {Modeling, analysis and simulation of ant-based network
                  routing protocols},
  journal =      swarm,
  volume =       4,
  number =       3,
  pages =        {221--244},
  year =         2010
}

@Article{TraMeh2009eo,
  author =       Trautmann #and# {J\"{o}rn Mehnen},
  title =        "Preference-based {Pareto} optimization in certain and
                  noisy environments",
  journal =      eo,
  volume =       41,
  number =       1,
  pages =        {23--38},
  month =        jan,
  year =         2009
}

@Article{TriLop2015plos,
  author =       Trianni_V #and# Lopez-Ibanez,
  title =        "Advantages of Task-Specific Multi-Objective Optimisation in
                  Evolutionary Robotics",
  journal =      pone,
  year =         2015,
  volume =       10,
  number =       8,
  pages =        {e0136406},
  doi =          {10.1371/journal.pone.0136406},
  abstract =     "The application of multi-objective optimisation to
                  evolutionary robotics is receiving increasing attention. A
                  survey of the literature reveals the different possibilities
                  it offers to improve the automatic design of efficient and
                  adaptive robotic systems, and points to the successful
                  demonstrations available for both task-specific and
                  task-agnostic approaches (i.e., with or without reference to
                  the specific design problem to be tackled). However, the
                  advantages of multi-objective approaches over
                  single-objective ones have not been clearly spelled out and
                  experimentally demonstrated. This paper fills this gap for
                  task-specific approaches: starting from well-known results in
                  multi-objective optimisation, we discuss how to tackle
                  commonly recognised problems in evolutionary robotics. In
                  particular, we show that multi-objective optimisation (i)
                  allows evolving a more varied set of behaviours by exploring
                  multiple trade-offs of the objectives to optimise, (ii)
                  supports the evolution of the desired behaviour through the
                  introduction of objectives as proxies, (iii) avoids the
                  premature convergence to local optima possibly introduced by
                  multi-component fitness functions, and (iv) solves the
                  bootstrap problem exploiting ancillary objectives to guide
                  evolution in the early phases. We present an experimental
                  demonstration of these benefits in three different case
                  studies: maze navigation in a single robot domain, flocking
                  in a swarm robotics context, and a strictly collaborative
                  task in collective robotics.",
}

@Article{TriNol2011al,
  author =       Trianni_V #and# {Nolfi, S.},
  title =        {Engineering the evolution of self-organizing
                  behaviors in swarm robotics: A case study},
  journal =      al,
  year =         2011,
  volume =       17,
  number =       3,
  pages =        {183--202},
  alias =        {Trianni:2011wl}
}

@Article{TriSriSanGho2016survey,
  title =        {A survey of multiobjective evolutionary algorithms based on
                  decomposition},
  author =       {Trivedi, Anupam and Srinivasan, Dipti and Sanyal, Krishnendu
                  and Ghosh, Abhiroop},
  journal =      tec,
  year =         2016,
  number =       3,
  pages =        {440--462},
  volume =       21,
  publisher =    {IEEE}
}

@Article{TseLin09,
  author =       Tseng_LY #and# Lin_YT,
  title =        "A hybrid genetic local search algorithm for the permutation
                  flowshop scheduling problem",
  journal =      ejor,
  volume =       198,
  number =       1,
  pages =        "84--92",
  year =         2009,
}

@Article{Tsu2007tjsai,
  author =       {Tsutsui, S.},
  title =        "Ant Colony Optimization with Cunning Ants",
  journal =      tjsai,
  keywords =     {ant colony optimization, traveling salesman problem, cunning
                  ant, donor ant, local search},
  year =         2007,
  volume =       22,
  pages =        {29--36},
  doi =          {10.1527/tjsai.22.29},
}

@Article{TugThrFil2017jbe,
  title =        {Conceptual Design of Modular Bridges Including Layout
                  Optimization and Component Reusability},
  author =       {Tugilimana, Alexis and Thrall, Ashley P. and Filomeno Coelho,
                  Rajan},
  journal =      {Journal of Bridge Engineering},
  volume =       22,
  number =       11,
  pages =        04017094,
  year =         2017,
  keywords =     "scenario-based",
  doi =          "10.1061/(ASCE)BE.1943-5592.0001138",
  publisher =    {American Society of Civil Engineers}
}

@Article{TurSorHva2021meta,
  author =       {Renata Turkeš} #and# Sorensen #and# {Lars Magnus Hvattum},
  title =        {Meta-analysis of metaheuristics: Quantifying the effect of
                  adaptiveness in adaptive large neighborhood search},
  journal =      ejor,
  volume =       292,
  number =       2,
  pages =        {423--42},
  year =         2021,
  doi =          {10.1016/j.ejor.2020.10.045},
  keywords =     {Metaheuristics, Meta-analysis, Adaptive large neighborhood
                  search},
}

@Article{TusFil2014mpe,
  author =       Tusar #and# Filipic,
  title =        {Visualizing Exact and Approximated {3D} Empirical Attainment
                  Functions},
  journal =      mpe,
  volume =       2014,
  note =         {Article ID 569346, 18 pages},
  year =         2014,
  doi =          "10.1155/2014/569346",
  comment =      {This is how Tea Tusar suggests to encode this entry in
                  bibtex.}
}

@Article{TusFil2015tec,
  title =        {Visualization of {Pareto} front approximations in
                  evolutionary multiobjective optimization: A critical review
                  and the prosection method},
  author =       Tusar #and# Filipic,
  journal =      tec,
  year =         2015,
  volume =       19,
  number =       2,
  pages =        {225--245},
  doi =          {10.1109/TEVC.2014.2313407}
}

@Article{Tuyttens00,
  author =       {D. Tuyttens} #and# Teghem #and# Fortemps #and# {K. Van Nieuwenhuyze},
  title =        {Performance of the {MOSA} Method for the Bicriteria Assignment Problem},
  journal =      joh,
  year =         2000,
  volume =       6,
  pages =        {295--310}
}

@Article{TveKah1974judgment,
  title =        {Judgment under uncertainty: Heuristics and biases},
  author =       Tversky #and# Kahneman,
  journal =      science,
  volume =       185,
  number =       4157,
  pages =        {1124--1131},
  year =         1974,
  publisher =    {American association for the advancement of science}
}

@Article{TveKah1991,
  author =       Tversky #and# Kahneman,
  year =         1991,
  title =        "Loss aversion in riskless choice: a reference-dependent
                  model",
  journal =      qje,
  volume =       106,
  number =       4,
  pages =        "1039--1061"
}

@Article{Tversky1972,
  author =       Tversky,
  year =         1972,
  title =        "Choice by elimination",
  journal =      jmatpsy,
  volume =       9,
  number =       4,
  pages =        "341--367"
}

@Article{TwoStuDorManBir2010is,
  author     =   {Colin Twomey} #and# Stuetzle #and# Dorigo #and# {Max Manfrin} #and#  Birattari,
  title      =   "An Analysis of Communication Policies for Homogeneous Multi-colony {ACO} Algorithms",
  journal =      is,
  year =         2010,
  volume =       180,
  number =       12,
  pages =        {2390--2404},
  doi =          {10.1016/j.ins.2010.02.017},
}

@Article{UluTeg1995,
  author =       Ulungu #and# Teghem,
  title =        {The two phases method: An efficient procedure
                  to solve bi-objective combinatorial optimization
                  problems},
  journal =      fcds,
  volume =       20,
  number =       2,
  pages =        {149--165},
  year =         1995
}

@Article{UluTegFor1999,
   author =      Ulungu #and# Teghem #and# { Fortemps, P. H. and Tuyttens, D.},
   title =       "{MOSA} method: a tool for solving multiobjective
                  combinatorial optimization problems",
   journal =     jmcda,
   number =      4,
   pages =       {221--236},
   volume =      8,
   year =        1999
}

@Article{UrlRuiSef2010,
  author = 	 {Thijs Urlings} #and# Ruiz_Ruben #and# {F. Sivrikaya-\c{S}erifo\u{g}lu},
  title = 	 {Genetic Algorithms for Complex Hybrid Flexible Flow Line Problems},
  journal = 	 ijmh,
  year = 	 2010,
  volume = 	 1,
  number = 	 1,
  pages = 	 {30--54}
}

@Article{UrlRuiStu2010ejor,
  author =       {Thijs Urlings} #and# Ruiz_Ruben #and# Stuetzle,
  title =        {Shifting Representation Search for Hybrid Flexible Flowline Problems},
  journal =      ejor,
  year =         2010,
  volume =       207,
  number =       2,
  pages =        {1086--1095},
  doi =          {10.1016/j.ejor.2010.05.041},
}

@Article{VaeAarLen1998cor,
  author =       Vaessens #and# Aarts #and# Lenstra,
  title =        "A Local Search Template",
  journal =      cor,
  volume =       25,
  number =       11,
  year =         1998,
  pages =        {969--979},
  doi =          {10.1016/S0305-0548(97)00093-2},
}

@Article{ValMarWanDeb2021mipdom,
  title =        {Analyzing dominance move ({MIP}-{DoM}) indicator for
                  multi-and many-objective optimization},
  author =       {do Val Lopes, Claudio Lucio and Martins, Fl{\'a}vio
                  Vin{\'i}cius Cruzeiro} #and# Wanner_E #and# Deb,
  journal =      tec,
  year =         2021,
  publisher =    {IEEE}
}

@Article{ValRui2010,
  author  = Vallada #and# Ruiz_Ruben,
  title   = {Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem},
  journal = omega,
  volume  = 38,
  number  = {1--2},
  pages   = {57--67},
  year    = 2010,
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}

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  year    =     2015,
  doi     =     {10.1016/j.ejor.2014.07.033},
}

@Article{ValRuiMin08,
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  url =          {http://arxiv.org/abs/1706.03762},
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@Article{WanJinSchOlh2021transfer,
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  publisher =    {Elsevier},
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@Article{Weyland2015,
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@Article{WooRitOpp2011ijmh,
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@Article{WuSchCheLeNorMacKriCaoGaoMac2016gnmt,
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}

@Article{XinCheChe2018review,
  author =       {Xin, B. and Chen, L. and Chen, J.}  #and# Ishibuchi #and#
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  journal =      ieee-access,
  title =        {Interactive Multiobjective Optimization: A Review of the
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  volume =       6,
  pages =        {41256--41279},
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  keywords =     {Decision making, Evolutionary computation, Pareto
                  optimization, Evolutionary multiobjective optimization,
                  interactive multiobjective optimization, multiple criteria
                  decision making, preference information, preference models},
  abstract =     {Interactive multiobjective optimization (IMO) aims at finding
                  the most preferred solution of a decision maker with the
                  guidance of his/her preferences which are provided
                  progressively. During the process, the decision maker can
                  adjust his/her preferences and explore only interested
                  regions of the search space. In recent decades, IMO has
                  gradually become a common interest of two distinct
                  communities, namely, the multiple criteria decision making
                  (MCDM) and the evolutionary multiobjective optimization
                  (EMO). The IMO methods developed by the MCDM community
                  usually use the mathematical programming methodology to
                  search for a single preferred Pareto optimal solution, while
                  those which are rooted in EMO often employ evolutionary
                  algorithms to generate a representative set of solutions in
                  the decision maker's preferred region. This paper aims to
                  give a review of IMO research from both MCDM and EMO
                  perspectives. Taking into account four classification
                  criteria including the interaction pattern, preference
                  information, preference model, and search engine (i.e.,
                  optimization algorithm), a taxonomy is established to
                  identify important IMO factors and differentiate various IMO
                  methods. According to the taxonomy, state-of-the-art IMO
                  methods are categorized and reviewed and the design ideas
                  behind them are summarized. A collection of important issues,
                  e.g., the burdens, cognitive biases and preference
                  inconsistency of decision makers, and the performance
                  measures and metrics for evaluating IMO methods, are
                  highlighted and discussed. Several promising directions
                  worthy of future research are also presented.},
}

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}

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  pages =        "565--606",
  month =        jun,
  epub =          {http://www.cs.ubc.ca/~hutter/papers/SATzilla.pdf},
  doi = 	 {10.1613/jair.2490}
}

@Article{XuLuChe2013,
  author =       {Hongyun Xu and Zhipeng L{\"u} and T. C. E. Cheng},
  title =        {Iterated Local Search for Single-machine Scheduling with Sequence-dependent Setup Times to Minimize Total Weighted Tardiness},
  journal =      jos,
  year =         2014,
  volume =       17,
  number =       3,
  pages =        {271--287}
}

@Article{XuYan2003ids,
  author       = Xu_DL #and# Yang_JB,
  title        = {Intelligent Decision System for Self-Assessment},
  journal      = jmcda,
  year         = 2003,
  volume       = 12,
  number       = 1,
  pages        = {43--60},
  doi          = {10.1002/mcda.343},
  abstract     = {Many small and medium enterprises (SMEs) in the UK use the
                  beta (Business Excellence Through Action) approach to the
                  EFQM Excellence Model to conduct business excellence
                  self-assessment, which is in essence a multiple criteria
                  decision analysis (MCDA) problem. This paper introduces a
                  decision support software package called Intelligent Decision
                  System (IDS) to implement the beta approach. It is
                  demonstrated in the paper that the IDS-beta package can
                  provide not only average scores but also the following
                  numerical results and graphical displays on: Distributed
                  assessment results to demonstrate the diversity of company
                  performances The performance range to cater for incomplete
                  assessment information Comparisons between current
                  performances and past performances, among different companies
                  among different action plans. Strengths and weaknesses The
                  IDS-beta package also provides a structured knowledge base to
                  help assessors to make judgements more objectively. The
                  knowledge base contains guidelines provided by the developers
                  of the beta approach, best practices gathered from research
                  on award winning organizations, evidence collected from
                  companies being assessed and comments provided by assessors
                  to record the reasons why a specific criterion is assessed to
                  a certain grade for a company. Four small UK companies, the
                  industry partners of the research project, have carried out
                  the preliminary self-assessment using the package. The
                  results and experience of the application are discussed at
                  the end of the paper.},
  keywords     = {decision support system, business excellence, MCDA, quality
                  award, self-assessment, the evidential reasoning approach}
}

@Article{YagKisIba2006,
  author = 	 Yagiura #and# {M. Kishida} #and# Ibaraki,
  title = 	 {A 3-Flip Neighborhood Local Search for the Set Covering Problem},
  journal = 	 ejor,
  year = 	 2006,
  volume = 	 172,
  number =       2,
  pages = 	 {472--499}
}

@Article{Yam2018parking,
  author =       {Yuki Yamada},
  title =        {How to Crack Pre-registration: Toward Transparent and Open
                  Science},
  doi =          {10.3389/fpsyg.2018.01831},
  year =         2018,
  month =        sep,
  publisher =    {Frontiers Media {SA}},
  volume =       9,
  journal =      {Frontiers in Psychology},
  keywords =     {HARKing; PARKing}
}

@Article{YanEmmDeu2019bayesian,
  author =       {Kaifeng Yang} #and# Emmerich #and# Deutz_AH #and#
                  Baeck_Thomas,
  journal =      swevo,
  title =        {Multi-Objective {Bayesian} Global Optimization using Expected
                  Hypervolume Improvement Gradient},
  year =         2019,
  month =        feb,
  pages =        {945--956},
  volume =       44,
  doi =          {10.1016/j.swevo.2018.10.007},
  keywords =     {Bayesian Optimisation with preferences},
  publisher =    {Elsevier {BV}},
}

@Article{YanKrePin00:js,
  author =       {Y. Yang and S. Kreipl and M. L. Pinedo},
  title =        {Heuristics for Minimizing Total Weighted Tardiness in Flexible Flow Shops},
  journal =      jos,
  year =         2000,
  volume =       3,
  number =       2,
  pages =        {89--108}
}

@Article{YanLiLiuZhe2013,
  author =       {S. Yang} #and# Li_Miqing #and# {X. Liu and J. Zheng},
  journal =      tec,
  title =        {A Grid-Based Evolutionary Algorithm for Many-Objective
                  Optimization},
  year =         2013,
  volume =       17,
  number =       5,
  pages =        {721--736},
  doi =          "10.1109/TEVC.2012.2227145",
  annote =       "epsilon-grid"
}

@Article{YeDoeWang2022ac,
  author       = {Furong Ye} #and# Doerr_C #and# Wang_H #and# Baeck_Thomas,
  title        = {Automated Configuration of Genetic Algorithms by Tuning for
                  Anytime Performance},
  journal      = tec,
  year         = 2022,
  volume       = 26,
  number       = 6,
  pages        = {1526--1538},
  doi          = {10.1109/TEVC.2022.3159087}
}

@Article{YuLin2015,
  author =       {Vincent F. Yu and Shih-Wei Lin},
  title =        {Iterated Greedy Heuristic for the Time-dependent
                  Prize-collecting Arc Routing Problem},
  journal =      cie,
  year =         2015,
  volume =       90,
  pages =        {54--66}
}

@Article{YuPowSte94,
  author =       Yu_G #and# Powell_RS #and# Sterling_MJH,
  title =        {Optimized Pump Scheduling in Water Distribution
                  Systems},
  journal =      jota,
  year =         1994,
  volume =       83,
  number =       3,
  pages =        {463--488},
}

@Article{YuaMonStuBir12:si,
  author =        Yuan_Z #and# MontesDeOca #and# Stuetzle #and# Birattari,
  title =        {Continuous Optimization Algorithms for Tuning Real and
Integer Algorithm Parameters of Swarm Intelligence Algorithms},
  journal =      swarm,
  year =         2012,
  volume =       6,
  number =       1,
  pages =        {49--75}
}

@Article{ZenYan2009cor,
  author =       {Zeng, Q. and Yang, Z.},
  title =        "Integrating Simulation and Optimization to Schedule
                  Loading Operations in Container Terminals",
  journal =      cor,
  year =         2009,
  volume =       36,
  number =       6,
  pages =        {1935--1944},
  doi =          {10.1016/j.cor.2008.06.010}
}

@Article{ZhaGeoAna2016,
  author = 	 {Tiantian Zhang and Michael Georgiopoulos and Georgios C. Anagnostopoulos},
  title = 	 {Multi-Objective Model Selection via Racing},
  journal = 	 tcyb,
  year = 	 2016,
  volume = 	 46,
  number = 	 8,
  pages = 	 {1863--1876},
}

@Article{ZhaLi07:moead,
  author =       Zhang_Qingfu #and# {Hui Li},
  title =        "{MOEA/D}: A Multiobjective Evolutionary Algorithm Based on
                  Decomposition",
  journal =      tec,
  year =         2007,
  volume =       11,
  number =       6,
  pages =        {712--731},
  doi =          {10.1109/TEVC.2007.892759},
  annote =       "Introduces penalty-based boundary intersection (PBI)
                  function"
}

@Article{ZhaSan2009jade,
  title =        {{JADE}: Adaptive differential evolution with optional
                  external archive},
  author =       {Zhang, Jingqiao} #and# {Sanderson, Arthur C.},
  journal =      tec,
  volume =       13,
  number =       5,
  pages =        {945--958},
  year =         2009,
  doi =          "10.1109/TEVC.2009.2014613"
}

@Article{ZhaSud2004dts,
  author =       {Zhao, H. and Sudha Ram},
  journal =      tkde,
  title =        {Constrained cascade generalization of decision trees},
  year =         2004,
  volume =       16,
  number =       6,
  pages =        {727--739},
  doi =          {10.1109/TKDE.2004.3}
}

@Article{ZheCha2012berth,
  title =        {A bi-objective model for robust berth allocation scheduling},
  author =       {Zhen, Lu and Chang, Dao-Fang},
  journal =      cie,
  volume =       63,
  number =       1,
  pages =        {262--273},
  year =         2012,
}

@Article{ZhoZhaJin2009igdx,
  author =       {Zhou, A.} #and# Zhang_Qingfu #and# Jin_Yaochu,
  title =        {Approximating the set of {Pareto}-optimal solutions in both
                  the decision and objective spaces by an estimation of
                  distribution algorithm},
  journal =      tec,
  year =         2009,
  volume =       13,
  number =       5,
  pages =        {1167--1189},
  doi =          "10.1109/TEVC.2009.2021467",
  keywords =     "multi-modal, IGDX",
}

@Article{Zilberstein96:anytime,
  author =       Zilberstein,
  title =        {Using Anytime Algorithms in Intelligent Systems},
  journal =      aim,
  year =         1996,
  volume =       17,
  number =       3,
  pages =        {73--83},
  epub =
                  {http://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1232},
  keywords =     "performance profiles",
  doi = 	 {10.1609/aimag.v17i3.1232},
  abstract = {Anytime algorithms give intelligent systems the capability to trade deliberation time for quality of results. This capability is essential for successful operation in domains such as signal interpretation, real-time diagnosis and repair, and mobile robot control. What characterizes these domains is that it is not feasible (computationally) or desirable (economically) to compute the optimal answer. This article surveys the main control problems that arise when a system is composed of several anytime algorithms. These problems relate to optimal management of uncertainty and precision. After a brief introduction to anytime computation, I outline a wide range of existing solutions to the metalevel control problem and describe current work that is aimed at increasing the applicability of anytime computation.},
}

@Article{ZioWal1983interactive,
  title =        {An interactive multiple objective linear programming method
                  for a class of underlying nonlinear utility functions},
  author =       {Zionts, Stanley} #and# Wallenius_J,
  journal =      ms,
  volume =       29,
  number =       5,
  pages =        {519--529},
  year =         1983,
  publisher =    informs
}

@Article{ZitThi99:spea,
  author =       Zitzler #and# Thiele,
  title =        {Multiobjective Evolutionary Algorithms: {A} Comparative Case
                  Study and the Strength {Pareto} Approach},
  journal =      tec,
  volume =       3,
  number =       4,
  pages =        {257--271},
  year =         1999,
  doi =          "10.1109/4235.797969",
  pdf =          {ZitzlerThiele99-SPEA.pdf},
  annote =       {Proposed SPEA,
                  \url{http://www.tik.ee.ethz.ch/sop/publicationListFiles/zt1999a.pdf}}
}

@Article{ZitThiBad2010tec,
  author =       Zitzler #and# Thiele #and# Bader_J,
  title =        "On Set-Based Multiobjective Optimization",
  journal =      tec,
  year =         2010,
  volume =       14,
  number =       1,
  pages =        {58--79},
  doi =          {10.1109/TEVC.2009.2016569},
  keywords =     "Performance assessment; Preference articulation; refinement;
                  Set Partitioning; Set-preference",
  annote =       "Proposed SPAM and explores combination of quality indicators"
}

@Article{ZitThiDeb2000ec,
  author =       Zitzler #and# Thiele #and# Deb,
  title =        {Comparison of Multiobjective Evolutionary
                  Algorithms: Empirical Results},
  journal =      ec,
  year =         2000,
  volume =       8,
  number =       2,
  pages =        {173--195},
  doi =          {10.1162/106365600568202},
  keywords =     {ZDT benchmark},
}

@Article{ZitThiLauFon2003:tec,
  author =       Zitzler #and# Thiele #and# Laumanns #and# Fonseca # and
                  # Grunert_da_Fonseca,
  title =        {Performance Assessment of Multiobjective Optimizers: an
                  Analysis and Review},
  journal =      tec,
  year =         2003,
  volume =       7,
  number =       2,
  amonth =       apr,
  pages =        "117--132",
  alias =        {perfassess},
  doi =          "10.1109/TEVC.2003.810758",
  annote =       "Proposed the combination of quality indicators; proposed epsilon-indicator"
}

@Article{ZloBirMeuDor2004:aor,
  author =       {M. Zlochin} #and# Birattari #and# { N. Meuleau }
                  #and# Dorigo,
  journal =      aor,
  number =       {1--4},
  pages =        {373--395},
  title =        {Model-Based Search for Combinatorial Optimization: A
                  Critical Survey},
  volume =       131,
  year =         2004
}

@Article{mlrMBO,
  title =        "{mlrMBO}: A Modular Framework for Model-Based Optimization of
                  Expensive Black-Box Functions",
  author =       Bischl_B #and# {Jakob Richter} #and# Bossek_J #and#
                  {Daniel Horn} #and# {Janek Thomas} #and# {Michel Lang},
  year =         2017,
  journal =      arxiv # {1703.03373 [stat.ML]},
  url =          {http://arxiv.org/abs/1703.03373},
}

@Article{msc:special-issue,
  author =       Cordon #and# Herrera_F #and# Stuetzle,
  title =        "Special Issue on Ant Colony Optimization: Models and
                  Applications",
  journal =      msc,
  year =         2002,
  volume =       9,
  number =       3,
  pages =        {137--268},
}

@Article{powel03:demand,
  author =       McCormick #and# Powell_RS,
  title =        {Optimal Pump Scheduling in Water Supply Systems with Maximum
                  Demand Charges},
  journal =      jwrpm,
  volume =       129,
  number =       5,
  pages =        {372--379},
  date =         "2003-09/2003-10",
  year =         2003,
  month =        sep #" / "#oct,
}

@Article{QuaGreLiuHu2007searching,
  title =        "Searching for multiobjective preventive maintenance
                  schedules: Combining preferences with evolutionary
                  algorithms",
  journal =      ejor,
  volume =       177,
  number =       3,
  pages =        "1969--1984",
  year =         2007,
  doi =          "10.1016/j.ejor.2005.12.015",
  author =       "Gang Quan and Garrison W. Greenwood and Donglin Liu and
                  Sharon Hu",
  keywords =     "Evolutionary computations, Scheduling, Utility theory,
                  Preventive maintenance, Multi-objective optimization,
                  ranking-based, interactive",
  abstract =     "Heavy industry maintenance facilities at aircraft service
                  centers or railroad yards must contend with scheduling
                  preventive maintenance tasks to ensure critical equipment
                  remains available. The workforce that performs these tasks
                  are often high-paid, which means the task scheduling should
                  minimize worker idle time. Idle time can always be minimized
                  by reducing the workforce. However, all preventive
                  maintenance tasks should be completed as quickly as possible
                  to make equipment available. This means the completion time
                  should be also minimized. Unfortunately, a small workforce
                  cannot complete many maintenance tasks per hour. Hence, there
                  is a tradeoff: should the workforce be small to reduce idle
                  time or should it be large so more maintenance can be
                  performed each hour? A cost effective schedule should strike
                  some balance between a minimum schedule and a minimum size
                  workforce. This paper uses evolutionary algorithms to solve
                  this multiobjective problem. However, rather than conducting
                  a conventional dominance-based Pareto search, we introduce a
                  form of utility theory to find Pareto optimal solutions. The
                  advantage of this method is the user can target specific
                  subsets of the Pareto front by merely ranking a small set of
                  initial solutions. A large example problem is used to
                  demonstrate our method."
}

@Article{ranger2015,
  author =       {Marvin N. Wright and Andreas Ziegler},
  title =        {{\rpackage{ranger}}: A Fast Implementation of Random Forests for High
                  Dimensional Data in {\proglang{C++}} and {\proglang{R}}},
  journal =      arxiv # {1508.04409 [stat.ML]},
  url =          {https://arxiv.org/abs/1508.04409},
  year =         2015,
}

@Article{ranger2017:jss,
  author =       {Marvin N. Wright and Andreas Ziegler},
  title =        {{\rpackage{ranger}}: A Fast Implementation of Random Forests for High
                  Dimensional Data in {\proglang{C++}} and {\proglang{R}}},
  journal =      jss,
  year =         2017,
  volume =       77,
  number =       1,
  pages =        {1--17},
  doi =          {10.18637/jss.v077.i01}
}

@Article{scikit-learn2011,
  title =        {Scikit-learn: Machine learning in {\proglang{Python}}},
  author =       {Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel,
                  V.  and Thirion, B. and Grisel, O. and Blondel, M. and
                  Prettenhofer, P.  and Weiss, R. and Dubourg, V. and
                  Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher,
                  M. and Perrot, M. and Duchesnay, E.},
  journal =      jmlr,
  volume =       12,
  pages =        {2825--2830},
  year =         2011
}

@Article{vanZyl04,
  author =       vanZyl #and# Savic #and# Walters,
  title =        {Operational Optimization of Water Distribution
                  Systems using a Hybrid Genetic Algorithm},
  journal =      jwrpm,
  year =         2004,
  volume =       130,
  number =       2,
  pages =        {160--170},
  month =        mar,
}