A pre-peer reviewed version of the following article: Bartz-Beielstein, T. and Branke, J. and Mehnen, J. and Mersmann, O.: Evolutionary Algorithms. WIREs Data Mining Knowl Discov 2014, 4:178- 195. doi:10.1002/widm.1124 is available for download at Cologne Open Science: http://opus.bsz-bw.de/fhk/volltexte/2015/77/
Evolutionary algorithm is an umbrella term used to describe population based stochastic direct search algorithms that in some sense mimic natural evolution. Prominent representatives are genetic algorithms, evolution strategies, evolu- tionary programming, and genetic programming. Based on the evolutionary cycle, similarities and differences between theses algorithms are described. We briefly discuss how evolutionary algorithms can be adapted to work well in case of multiple objectives, dynamic or noisy optimization problems. We look at the tuning of algorithms and present some recent developments from theory. Finally, typical applications of evolutionary algorithms for real-world problems are shown, with special emphasis on data mining applications.