A Fuzzy Clustering Based Selection Method to Maintain Diversity in Genetic Algorithms
Yoshiaki Sakakura, N. Taniguchi, Yukinobu Hoshino, K. Kamei · 2006
Optimization requirements often include finding various solutions and search under muti-objective situations. A maintaining diversity of individuals is one of the effective approaches to meet the requirements. Our research aims to maintain the diversity. We also propose a fuzzy clustering based selection method to maintain the diversity and apply the selection method to genetic algorithm (GA). The selection method determines the individual selection probabilities based on fitness values and membership values, which are given by a fuzzy clustering. Here, a preparing a sub-population is one of the effective ways to maintain the diversity. The proposed selection method is treated as a getting the sub-population method by the fuzzy clustering. 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.