Evolution Strategies

Michael Emmerich (), Ofer M. Shir () and Hao Wang ()
Additional contact information
Michael Emmerich: University of Jyväskylä
Ofer M. Shir: Tel-Hai College, Computer Science Department
Hao Wang: Leiden University, Leiden Institute of Advanced Computer Science

Chapter 5 in Handbook of Heuristics, 2025, pp 89-123 from Springer

Abstract: 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.

Keywords: Evolution strategy; Evolutionary algorithms; Self-adaptation; Covariance matrix adaptation; Step size adaptation (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-00385-0_13

Ordering information: This item can be ordered from
http://www.springer.com/9783032003850

DOI: 10.1007/978-3-032-00385-0_13

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().