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Abstract: Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function optimization, multiobjective function optimization, and simulation of complex and adaptive systems. This study presents a comprehensive treatment of niching methods and the related topic of population diversity. Its purpose is to analyze existing niching methods and to design improved niching methods.... (Update)
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BibTeX entry: (Update)
S. W. Mahfoud, "Niching methods for genetic algorithms," IlliGAL Report No. 95001, University of Illinois at Urbana-Champaign, Urbana, IL, May 1995. http://citeseer.ist.psu.edu/mahfoud95niching.html More
@phdthesis{ mahfoud95niching,
author = "Samir W. Mahfoud",
title = "Niching methods for genetic algorithms",
address = "Urbana, IL, USA",
year = "1995",
url = "citeseer.ist.psu.edu/mahfoud95niching.html" }
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