Evolutionary algorithms

Thomas Bartz-Beielstein, Jürgen Branke, Jörn Mehnen, Olaf Mersmann

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Evolutionary algorithm (EA) is an umbrella term used to describe population-based stochastic direct search algorithms that in some sense mimic natural evolution. Prominent representatives of such algorithms are genetic algorithms, evolution strategies, evolutionary programming, and genetic programming. On the basis of the evolutionary cycle, similarities and differences between these algorithms are described. We briefly discuss how EAs can be adapted to work well in case of multiple objectives, and dynamic or noisy optimization problems. We look at the tuning of algorithms and present some recent developments coming from theory. Finally, typical applications of EAs to real-world problems are shown, with special emphasis on data-mining applications
Original languageEnglish
Pages (from-to)178–195
Number of pages18
JournalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Volume4
Issue number3
Early online date24 Apr 2014
DOIs
Publication statusPublished - 31 May 2014

Keywords

  • evolutionary algorithm
  • overview

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