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Genetic algorithms and neighbourhood search

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Evolutionary Computing (AISB EC 1994)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 865))

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Abstract

Genetic algorithms (GAs) have proved to be a versatile and effective approach for solving combinatorial optimization problems. Nevertheless, there are many situations in which the simple GA does not perform particularly well, and various methods of hybridization have been proposed. These often involve incorporating other methods such as simulated annealing or local optimization as an ‘add-on’ extra to the basic GA strategy of selection and reproduction.

Here, we explore an alternative perspective which views genetic algorithms as a generalization of neighbourhood search methods. It is not the intention to present a fully worked-out statement as to what sort of neighbourhood search a GA is. Rather, it is to investigate some of the parallels, and to suggest some areas for further research which may enhance our understanding of both neighbourhood search and genetic algorithms.

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Authors and Affiliations

  1. School of Mathematical and Information Sciences, Coventry University, UK

    Colin R. Reeves

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  1. Colin R. Reeves
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Terence C. Fogarty

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© 1994 Springer-Verlag Berlin Heidelberg

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Reeves, C.R. (1994). Genetic algorithms and neighbourhood search. In: Fogarty, T.C. (eds) Evolutionary Computing. AISB EC 1994. Lecture Notes in Computer Science, vol 865. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58483-8_10

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  • DOI: https://doi.org/10.1007/3-540-58483-8_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58483-4

  • Online ISBN: 978-3-540-48999-3

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