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Hyperheuristics are tools that allow the generation of methodologies for solving certain problems. There are two types of hyperheuristics, Generation and Selection; although the objective of both is to generate methodologies, their inputs change, the selection ones require a series of low level heuristics while those of Generation require a series of heuristic elements which describe the problem. These hyperheuristics are composed by a core which is usually an optimization algorithm, which by nature has a series of parameters. The optimization of these parameters is a branch of study that aims to find those values under which the algorithm has the best performance in an instance of a problem. In the present work we propose the use of an Evolutionary Grammar (GE) with a search engine based on an Evolution Strategy (ES) as the core of a generation hyperheuristic because of it’s self-adaptive nature that allows to eliminate the process of parameter optimization. The proposal is applied to the Bin Packing Problem and the results are compared using Friedman’s non-parametric statistical test against those obtained by a search engine hyperheuristic based on Evolutionary Grammar using Particle Swarm Optimization (PSO) and Particle Evolutionary Swarm Optimization (PESO).

Gramática evolutiva con estrategia evolutiva como núcleo de una hiperheurística de generación aplicada al problema de empacado