The Univariate Marginal Distribution Algorithm Copes Well with Deception and Epistasis

In their recent work, Lehre and Nguyen (2019) show that the univariate marginal distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceptiveLeadingBlocks (DLB) problem. They conclude from this result that univariate EDAs have difficulties with decepti...

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Bibliographic Details

Published in:	Evolutionary computation Vol. 29; no. 4; p. 543
Main Authors:	Doerr, Benjamin, Krejca, Martin S
Format:	Journal Article
Language:	English
Published:	United States 01.12.2021
Subjects:	Algorithms Biological Evolution Epistasis, Genetic Models, Genetic Population Density Estimation-of-distribution algorithm runtime analysis univariate marginal distribution algorithm epistasis theory
ISSN:	1530-9304, 1530-9304
Online Access:	Get more information
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