Investigation of Genetic Algorithms with Self-Adaptive Crossover, Mutation, and Selection

A method of self-adaptive mutation, crossover and selection was implemented and applied in four genetic algorithms. So developed self-adapting algorithms were then compared, with respect to convergence, with a traditional genetic one, which contained constant rates of mutation and crossover. The experiments were conducted on six benchmark functions including two unimodal functions, three multimodal with many local minima, and one multimodal with a few local minima. The analysis of the results obtained was supported by statistical nonparametric Wilcoxon signed-rank tests. The algorithm employing self-adaptive selection revealed the best performance.