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...

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Bibliographic Details
Published in:Cognitive systems research 2000-06, Vol.1 (2), p.119-133
Main Authors: El Imrani, A., Bouroumi, A., Zine El Abidine, H., Limouri, M., Essaı̈d, A.
Format: Article
Language:English
Subjects:
Fuzzy clustering
Genetic algorithms
Multimodal optimization
Self learning
Sharing
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Summary: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.
ISSN:1389-0417
1389-0417
DOI:10.1016/S1389-0417(99)00013-3