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Evolution Strategies

In: Handbook of Heuristics

Author

Listed:
  • Michael Emmerich

    (University of Jyväskylä)

  • Ofer M. Shir

    (Tel-Hai College, Computer Science Department
    The Galilee Research Institute – Migal)

  • Hao Wang

    (Leiden University, Leiden Institute of Advanced Computer Science)

Abstract

Evolution strategies (ESs) 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 ESs will be provided. The review includes remarks on the history of ESs and how they relate to other evolutionary algorithms. Furthermore, developments of ESs for nonstandard problems and search spaces will also be summarized, including multimodal, multi-criterion, and mixed-integer optimization. Finally, selected variants of ESs are compared on a representative set of continuous benchmark functions, revealing strengths and weaknesses of the different variants.

Suggested Citation

  • Michael Emmerich & Ofer M. Shir & Hao Wang, 2025. "Evolution Strategies," Springer Books, in: Rafael Martí & Panos M. Pardalos & Mauricio G.C. Resende (ed.), Handbook of Heuristics, edition 0, chapter 5, pages 89-123, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-00385-0_13
    DOI: 10.1007/978-3-032-00385-0_13
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