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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. WIREs Data Mining Knowl Discov 2014, 4:178–195. doi: 10.1002/widm.1124 This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Fundamental Concepts of Data and Knowledge > Knowledge Representation
Bartz–Beielstein et al. (Thu,) studied this question.