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Abstract: This paper considers the related algorithms, crowding and preselection, as potential multimodal function optimizers. It examines the ability of the two algorithms to preserve diversity, especially multimodal diversity. Crowding is analyzed in terms of the number of replacement errors it makes. Different strategies for reducing or eliminating error are proposed and examined. Finally, a variation of preselection is presented which approximates crowding, virtually eliminates replacement error, and ... (Update)
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BibTeX entry: (Update)
Mahfoud, S. W. (1992). Crowding and preselection revisited. In Manner, R., & Manderick, B. (Eds.), Parallel Problem Solving from Nature, 2 (pp. 27--36). Amsterdam, The Netherlands: Elsevier Science. http://citeseer.ist.psu.edu/mahfoud92crowding.html More
@inproceedings{ mahfoud92crowding,
author = "S. W. Mahfoud",
title = "Crowding and preselection revisited",
booktitle = "Parallel problem solving from nature 2",
publisher = "North-Holland",
address = "Amsterdam",
editor = "Reinhard M{\"{a}}nner and Bernard Manderick",
pages = "27--36",
year = "1992",
url = "citeseer.ist.psu.edu/mahfoud92crowding.html" }
Citations (may not include all citations):209 Genetic algorithms with sharing for multimodal function opti.. (context) - Goldberg, Richardson - 1987 ACM
192 An analysis of the behavior of a class of genetic adaptive s.. (context) - De Jong - 1975
49 Finite Markov chain analysis of genetic algorithms (context) - Goldberg, Segrest - 1987 ACM DBLP
34 Genetic algorithms in multimodal function optimization (context) - Deb - 1989
27 Adaptive search using simulated evolution (context) - Cavicchio - 1970
3 Representation and hidden bias: Gray versus binary coding fo.. (context) - Caruana, Schaffer - 1988
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