Evolutionary algorithms

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

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)


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
Issue number3
Early online date24 Apr 2014
Publication statusPublished - 31 May 2014


  • evolutionary algorithm
  • overview


Dive into the research topics of 'Evolutionary algorithms'. Together they form a unique fingerprint.

Cite this