Abstract
Evolutionary algorithms (EAs) are global, parallel, search and optimization methods, founded on the principles of natural selection and population genetics. In general, any iterative, population based approach that uses selection and random variation to generate new solutions can be regarded as an EA. The evolutionary algorithms field has its origins in four landmark evolutionary approaches: evolutionary programming (EP), evolution strategies (ES), genetic algorithms (GA), and genetic programming (GP). The genetic algorithm was popularized by Goldberg (1989) and, as a result, the majority of control applications in the literature adopt this approach.KeywordsGenetic AlgorithmMutation OperatorCrossover ProbabilityRoulette WheelTournament SelectionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Published Version
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