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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Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 24; no. 6; pp. 1140 - 1149
Main Authors:	Doerr, Benjamin, Zheng, Weijie
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Subjects:	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 Access:	Get full text
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