Using supportive coevolution to evolve self-configuring crossover

Abstract

Creating an Evolutionary Algorithm (EA) which is capable of automatically configuring itself and dynamically controlling its parameters is a challenging problem. However, solving this problem can reduce the amount of manual configuration required to implement an EA, allow the EA to be more adaptable, and produce better results on a range of problems without requiring problem specific tuning. Using Supportive Coevolution (SuCo) to evolve Self-Configuring Crossover (SCX) combines the automatic configuration technique of multiple populations from SuCo with the dynamic crossover operator creation and evolution of SCX.This paper reports an empirical comparison and analysis of several different combinations of mutation and crossover techniques including SuCo and SCX. The Rosenbrock, Rastrigin, and Offset Rastrigin benchmark problems were selected for testing purposes. The benefits and drawbacks of self-adaptation and evolution of SCX are also discussed. SuCo of mutation step sizes and SCX operators produced results that were at least as good as previous work, and some experiments produced results that were significantly better.

Tutorial Sobre o Uso de Técnicas para Controle de Parâmetros em Algoritmos de Inteligência de Enxame e Computação Evolutiva

“…Já no controle online, uma característica importante é que o valor de um parâmetro pode variar de acordo com o estágio da busca [54]. Possuir controle online durante o processo de otimização pode melhorar significamente o resultado final e isto tem motivado a pesquisa por métodos para auto-ajustar os parâmetros inerentes às diferentes metaheurísticas [61,71,138,5,74,127,84]. O foco deste trabalho se concentra na revisão de técnicas para controle online.…”