Merge pull request #5 from danielskatz/patch-1

minor changes for final JOSS publication

paper.bib

@@ -21,7 +21,7 @@ @article{bayraktar2013wind
 
 @misc{hansen2019pycma,
   author       = {Nikolaus Hansen and Youhei Akimoto and Petr Baudis},
-  title        = {{CMA-ES/pycma} on {G}ithub},
+  title        = {{CMA-ES/pycma} on {G}it{H}ub},
   howpublished = {Zenodo, DOI:10.5281/zenodo.2559634},
   month        = feb,
   year         = 2019,
@@ -180,7 +180,7 @@ @Misc{gpyopt2016
 }
 
 @inproceedings{balandat2020botorch,
-  title={BoTorch: A Framework for Efficient Monte-Carlo {B}ayesian Optimization},
+  title={BoTorch: A Framework for Efficient {M}onte-{C}arlo {B}ayesian Optimization},
   author={Balandat, Maximilian and Karrer, Brian and Jiang, Daniel R. and Daulton, Samuel and Letham, Benjamin and Wilson, Andrew Gordon and Bakshy, Eytan},
   booktitle = {Advances in Neural Information Processing Systems 33},
   year={2020},
@@ -269,7 +269,7 @@ @software{Roith_polarcbo
 }
 
 @online{duan2023pypop7,
-  title={PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization},
+  title={PyPop7: A Pure-{P}ython Library for Population-Based Black-Box Optimization},
   author={Duan, Qiqi and Zhou, Guochen and Shao, Chang and Wang, Zhuowei and Feng, Mingyang and Yang, Yijun and Zhao, Qi and Shi, Yuhui},
   eprint={2212.05652},
   eprinttype={arXiv},
@@ -568,7 +568,6 @@ @InBook{yang2009firefly
   pages        = {169--178},
   publisher    = {Springer Berlin Heidelberg},
   issn         = {1611-3349},
-  organization = {Springer},
   year         = {2009},
 }
 

paper.md

@@ -116,7 +116,7 @@ CBX methods have been successfully applied and extended to several different set
 
 # Statement of need
 
-In general, very few implementations of CBO already exist, and none have been designed with the generality of other CBX methods in mind. We summarise here the related software:
+In general, very few implementations of CBO already exist, and none have been designed with the generality of other CBX methods in mind. Here, we summarise the related software:
 
 Regarding Python, we refer to `PyPop7` [@duan2023pypop7] and `scikit-opt` [@scikitopt] for a collection of various derivative-free optimisation strategies. For packages connected to Bayesian optimisation, we refer to `BayesO` [@Kim2023], `bayesian-optimization` [@Bayesian14], `GPyOpt` [@gpyopt2016], `GPflowOpt` [@GPflowOpt2017], `pyGPGO` [@Jiménez2017], `PyBADS` [@Singh2024] and `BoTorch` [@balandat2020botorch]. Furthermore, CMA-ES [@hansen1996adapting] was implemented in `pycma` [@hansen2019pycma]. To the best of our knowledge the connection between consensus-based methods and evolution strategies is not fully understood, and is therefore an interesting future direction. PSO and SA implementations are already available in `PySwarms` [@miranda2018pyswarms], `scikit-opt` [@scikitopt], `DEAP` [@deapJMLR2012] and `pagmo` [@pagmo2017]. They are widely used by the community and provide a rich framework for the respective methods. However, adjusting these implementations to CBO is not straightforward. The first publicly available Python packages implementing CBX algorithms were given by some of the authors together with collaborators. @Igor_CBOinPython implement standard CBO [@pinnau2017consensus], and the package `PolarCBO` [@Roith_polarcbo] provides an implementation of polarised CBO [@bungert2022polarized]. [CBXPy](https://pdips.github.io/CBXpy/) is a significant extension of the latter, which was tailored to the polarised variant. The code architecture was generalised, which allowed the implementation of the whole CBX family within a common framework.