Selection Scheme for Maintaining Diversity in Genetic Algorithms Using Fuzzy c-Means

Abstract

Optimization requirements often include a finding various solutions and a searching under muti-objective situations. A maintaining diversity of individuals is one of the effective approaches to meet the requirements, when we use Genetic Algorithms (GAs) for these optimization. Our research aims to maintain the diversity. We propose a new selection scheme for maintaining the diversity and apply the selection to simple GA (sGA). In our selection, the individuals are classified by Fuzzy c-Means. Accordingly, several clusters come up and each of the individuals gets a membership value for each of the clusters. The proposed method selects individuals based on both the fitness values and the membership values. We also discuss about behavior and search capability of the GA with the proposed selection method via some simulations. Based on results of the simulations, we were able to find out that the GA makes the individuals widely distributed in a solution space.