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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Summary: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 frequencies to boundary values not justified by the fitness. This can result in a considerable performance loss. This article gives the first tight quantification of this effect for three EDAs and one ant colony optimizer, namely, for the univariate marginal distribution algorithm, the compact genetic algorithm, population-based incremental learning, and the max-min ant system with iteration-best update. Our results allow to choose the parameters of these algorithms in such a way that within a desired runtime, no sampling frequency approaches the boundary values without a clear indication from the objective function.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2020.2987361