A Fast Genetic Algorithm with Sharing Scheme Using Cluster Analysis Methods in Multimodal Function Optimization

Abstract

Genetic algorithms with sharing are well known for tackling multimodal function optimization problems. In this paper, a sharing scheme using a clustering methodology is introduced and compared with the classical sharing scheme. It is shown from the simulation on test functions and on a practical problem that the proposed scheme proceeds faster than the classical scheme with a performance remaining as good as the classical one. In addition, the proposed scheme reveals unknown multimodal function structure when a priori knowledge about the function is poor. Finally, introduction of a mating restriction inside the proposed scheme is investigated and shown to increase the optimization quality without requiring additional computation efforts.