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.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Evolution Strategies

  • Michael Emmerich,
  • Ofer M. Shir,
  • Hao Wang

摘要

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.