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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Veröffentlicht in:	Evolutionary computation Jg. 29; H. 4; S. 543
Hauptverfasser:	Doerr, Benjamin, Krejca, Martin S
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 01.12.2021
Schlagworte:	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-Zugang:	Weitere Angaben
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