Adaptive Genetic Algorithm Based on Density Distribution of Population

Abstract

The control parameters in evolutionary algorithms (EAs) have significant effects on the behavior and performance of the algorithm. Most existing parameter control mechanisms are based on either individual fitness or positional distribution of population. This paper proposes a parameter adaptation strategy which aims at evaluating the density distribution of population as well as both the fitness values comprehensively, and adapting the parameters accordingly. The proposed method modifies the values of px and pm based on the relative cluster density and the relative sizes of clusters containing the best and the worst individuals.