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2018 | OriginalPaper | Buchkapitel

4. Evolution Strategies

verfasst von : Michael Emmerich, Ofer M. Shir, Hao Wang

Erschienen in: Handbook of Heuristics

Verlag: Springer International Publishing

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Abstract

Evolution strategies are classical variants of evolutionary algorithms which are frequently used to heuristically solve optimization problems, in particular in continuous domains. In this chapter, a description of classical and contemporary evolution strategies will be provided. The review includes remarks on the history of evolution strategies and how they relate to other evolutionary algorithms. Furthermore, developments of evolution strategies for nonstandard problems and search spaces will also be summarized, including multimodal, multi-criterion, and mixed-integer optimization. Finally, selected variants of evolution strategies are compared on a representative set of continuous benchmark functions, revealing strength and weaknesses of the different variants.

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Vorheriges Kapitel Data Mining in Stochastic Local Search

Nächstes Kapitel Matheuristics

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Titel: Evolution Strategies
verfasst von: Michael Emmerich
Ofer M. Shir
Hao Wang
Verlag: Springer International Publishing
Buch: Handbook of Heuristics
Print ISBN: 978-3-319-07123-7

Electronic ISBN: 978-3-319-07124-4

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-07124-4_13

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