Evolutionary Algorithms. (24th April 2014)

• Record Type: Journal Article
• Authors: Bartz‐Beielstein, Thomas; Branke, Jürgen; Mehnen, Jörn; Mersmann, Olaf
• Is Part Of: Wiley interdisciplinary reviews. Volume 4:Number 3(2014)
• Page Start: 178
• Page End: 195
• Publication Date: 2014-04-24
• Subjects: Data mining -- Periodicals; 006.31205
• DOI: 10.1002/widm.1124
• Languages: English
• ISSNs: 1942-4787

Abstract

<abstract abstract-type="main" id="widm1124-abs-0001"> <title>Abstract</title> <p id="widm1124-para-0001">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. <italic>WIREs Data Mining Knowl Discov</italic> 2014, 4:178–195. doi: 10.1002/widm.1124</p> <p id="widm1124-para-0002">Conflict of interest: The authors have declared no conflicts of interest for this article.</p> <p>For further resources related to this article, please visit the <ext-link ext-link-type="uri" xlink:href="http://wires.wiley.com/remdoi.cgi?doi=10.1002/widm.1124" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink">WIREs website</ext-link>.</p> </abstract>