Sharp Bounds for Genetic Drift in Estimation of Distribution Algorithms

Estimation of distribution algorithms (EDAs) are a successful branch of evolutionary algorithms (EAs) that evolve a probabilistic model instead of a population. Analogous to genetic drift in EAs, EDAs also encounter the phenomenon that the random sampling in the model update can move the sampling fr...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 24; H. 6; S. 1140 - 1149
Hauptverfasser:	Doerr, Benjamin, Zheng, Weijie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Schlagworte:	Computational modeling Computer Science Drift estimation Estimation of distribution algorithms (EDAs) Evolutionary algorithms Genetic algorithms genetic drift Genetics Iterative methods Machine learning Probabilistic logic Probabilistic models Random sampling running time analysis Sociology Statistics theory Time-frequency analysis
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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