- Alejandro Marrero --- @amarrerod
- Eduardo Segredo --- @esegredo
- Coromoto León --- @cleon
Deciding which optimisation technique to use for solving a particular optimisation problem is an important and arduous task that has been faced in the field of optimisation for decades. The above problem is known as the Algorithm Selection Problem (ASP). Many researchers have tried to solve the ASP for a wide variety of problems. The optimisation techniques considered in previous works have been, mainly, optimisation techniques that can be executed in a fast manner. However, considering more sophisticated optimisation approaches for solving the ASP, such as Evolutionary Algorithms, drastically increases the computational cost involved. We are interested in solving the ASP by considering different configurations of a Genetic Algorithm (GA) applied to the well-known NP-hard 0/1 Knapsack Problem (KNP). The above involves the execution of a significant number of configurations of the said GA, in order to evaluate their performance, when applied to a wide range of instances with different features of the KNP, which is a computationally expensive task. Therefore, the main aim of the current work is to provide, as first step for solving the ASP, an efficient parallel GA, which is able to attain competitive results, in terms of the optimal objective value, in a short amount of time. Computational results show that our approach is able to scale efficiently and considerably reduces the average elapsed time for solving KNP instances.