Evolution Strategies

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

Chapter 4 in Handbook of Heuristics, 2018, pp 89-119 from Springer

Abstract: 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.

Keywords: Evolution strategy; derandomization; CMA-ES; benchmarking; theory (search for similar items in EconPapers)
Date: 2018
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:978-3-319-07124-4_13

Ordering information: This item can be ordered from
http://www.springer.com/9783319071244

DOI: 10.1007/978-3-319-07124-4_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 ().