Genetic algorithms with dynamic niche sharing for multimodal function optimization

Brad L. Miller, Michael Jeng-Ping Shaw

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Genetic algorithms utilize populations of individual hypotheses that converge over time to a single optimum, even within a multimodal domain. This paper examines methods that enable genetic algorithms to identify multiple optima within multimodal domains by maintaining population members within the niches defined by the multiple optima. A new mechanism, Dynamic Niche Sharing, is developed that is able to efficiently identify and search multiple niches (peaks) in a multimodal domain. Dynamic niche sharing is shown to perform better than two other methods for multiple optima identification, Standard Sharing and Deterministic Crowding.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Conference on Evolutionary Computation
PublisherIEEE
Pages786-791
Number of pages6
StatePublished - 1996
EventProceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96 - Nagoya, Jpn
Duration: May 20 1996May 22 1996

Other

OtherProceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96
CityNagoya, Jpn
Period5/20/965/22/96

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Miller, B. L., & Shaw, M. J-P. (1996). Genetic algorithms with dynamic niche sharing for multimodal function optimization. In Proceedings of the IEEE Conference on Evolutionary Computation (pp. 786-791). IEEE.