A coevolutionary genetic algorithm using fuzzy clustering

Abstract

During the last few years, fuzzy logic and Genetic algorithms (GAs) have been experiencing extremely rapid growth in the industrial world, where they have been shown to be very effective in solving real-world problems. In this paper, we present a coevolutionary inspired method, which combines sharing GA with a fuzzy clustering technique for multimodal function optimization. Without using any prior knowledge, this approach allows both location and maintenance of niches. Since the niche radii are continuously updated, a fine local tuning is also performed. Several well-known functions are used to test the performances of the proposed algorithm.