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# Evolutionary Algorithms

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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. WIREs Data Mining Knowl Discov 2014, 4:178–195. doi: 10.1002/widm.1124

Conflict of interest: The authors have declared no conflicts of interest for this article.

The evolutionary cycle, basic working scheme of all EAs. Common terms for describing ES are used, alternative terms (crossover, replacement) are added below.
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Illustration of (marginal) hypervolume.
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Crossover via subtree exchanging applied to two GP individuals (a) and (b), which are represented as binary trees. The first tree represents the S‐expression ‘(−(11 x))’, the second tree represents ‘(+(1 (* (x 3))))’. The dashed lines denote the positions, where crossover takes place. Two offspring are created: (c), which represents ‘(−(11 (* (x 3))))’ and (d), which represents the S‐expression ‘(+(1 x))’.
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Contour plot of f(x) and $f˜x$ illustrating the effect of rotation on the function landscape.
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