An Overview on Evolutionary Algorithms
Bartz-Beielstein, Thomas, Branke, Jürgen, Mehnen, Jörn, Mersmann, Olaf · Kluwer Academic Publishers eBooks · 2005
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, evolutionary 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. Evolutionary Algorithms in a NutshellInvention and development of the first evolutionary algorithms is nowadays attributed to a few pioneers who independently suggested four related approaches (Bartz-Beielstein et al., 2010b).• Fogel et al. (1965) introduced evolutionary programming (EP) aiming at evolving finite automata, later at solving numerical optimization problems.