Investigation of self-adapting genetic algorithms using some multimodal benchmark functions

Abstract

Self-adaptive mutation, crossover, and selection were implemented and applied in three genetic algorithms. So developed self-adapting algorithms were then compared, with respect to convergence, with a standard genetic one, which contained constant rates of mutation and crossover. The experiments were conducted using five multimodal benchmark functions. The analysis of the results obtained was supported by nonparametric Friedman and Wilcoxon signed-rank tests. The algorithm employing self-adaptive selection revealed the best performance. © 2011 Springer-Verlag Berlin Heidelberg.