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PAL-ULL/GECCO-21-Parallel-GA-KNP
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- Alejandro Marrero ---@amarrerod
- Eduardo Segredo ---@esegredo
- Coromoto León ---@cleon
Deciding which optimisation technique to use forsolving a particular optimisation problem is an important andarduous task that has been faced in the field of optimisation fordecades. The above problem is known as the Algorithm SelectionProblem (ASP). Many researchers have tried to solve the ASPfor a wide variety of problems. The optimisation techniquesconsidered in previous works have been, mainly, optimisationtechniques that can be executed in a fast manner. However, considering more sophisticated optimisation approaches for solvingthe ASP, such as Evolutionary Algorithms, drastically increasesthe computational cost involved. We are interested in solvingthe ASP by considering different configurations of a GeneticAlgorithm (GA) applied to the well-known NP-hard 0/1 KnapsackProblem (KNP). The above involves the execution of a significantnumber of configurations of the said GA, in order to evaluatetheir performance, when applied to a wide range of instanceswith different features of the KNP, which is a computationallyexpensive task. Therefore, the main aim of the current work isto provide, as first step for solving the ASP, an efficient parallelGA, which is able to attain competitive results, in terms of theoptimal objective value, in a short amount of time. Computationalresults show that our approach is able to scale efficiently andconsiderably reduces the average elapsed time for solving KNPinstances.
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Paper to be submitted to GECCO 2021
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