Evolutionary Multimodal Optimization

Mykola M. Glybovets () and Nataliya M. Gulayeva ()
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Mykola M. Glybovets: National University of Kyiv-Mohyla Academy
Nataliya M. Gulayeva: National University of Kyiv-Mohyla Academy

A chapter in Optimization Methods and Applications, 2017, pp 137-181 from Springer

Abstract: Abstract In this chapter, a comprehensive review of niching genetic algorithms designed to solve multimodal optimization problems is given. First, an introduction to multimodal optimization problem and to niching is provided. After that, a number of niching algorithms are discussed. These algorithms are presented according to their spatial-temporal classification, although other classifications are also mentioned. Methods analyzed in detail among others include sequential niching, fitness sharing, clearing, multinational genetic algorithm, clustering, species conserving genetic algorithm, crowding (standard, deterministic, probabilistic, multi-niche), restricted tournament selection, and others. Most methods are followed by their numerous modifications. The efficiency of hybridization of different algorithms is discussed, and examples of such hybridization are provided. Experimental approach to analyze performance of niching algorithms is described. To estimate the ability of the algorithms in finding and maintaining multiple optima, most popular test criteria and benchmark problems are given.

Keywords: Fitness Sharing; Sequential Niche; Restricted Tournament Selection; Genetic Algorithm; Crowding Methods (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-319-68640-0_8

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DOI: 10.1007/978-3-319-68640-0_8

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