A fuzzy clustering-based niching approach to multimodal function optimization
This paper presents a new method, which combines sharing and a fuzzy clustering technique to improve the performance of genetic algorithms in multimodal function optimization. This approach permits some limitations of the traditional sharing scheme to be overcome. Without using any prior information, it allows both location and maintenance of niches. Computer simulations show good performance for several multimodal test functions including a deceptive problem.
fuzzy clustering sharing genetic algorithms multimodal optimization self learning