The Competing Genes Evolutionary Algorithm: Avoiding Genetic Drift Through Competition, Local Search, and Majority Voting

An estimation-of-distribution algorithm is an evolutionary algorithm that uses a probabilistic model to represent its population. It iteratively draws solutions from its model and, considering their fitnesses, uses these solutions to update the model's parameters. However, there is a risk of re...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 27; H. 6; S. 1678 - 1689
Hauptverfasser:	Ajimakin, Adetunji David, Devi, V. Susheela
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Computational modeling Estimation-of-distribution algorithms (EDAs) Evolutionary algorithms Evolutionary computation Gauss–Southwell rule Genes Genetic algorithms genetic drift Genetics Iterative methods local search majority voting Markov processes natural gradient Parameters Probabilistic models Random noise Runtime runtime analysis Search process Sociology Statistics Upper bounds
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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