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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| Published in: | IEEE transactions on evolutionary computation Vol. 24; no. 6; pp. 1140 - 1149 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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New York
IEEE
01.12.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers |
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| ISSN: | 1089-778X, 1941-0026 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Zheng, Weijie Doerr, Benjamin |
| Author_xml | – sequence: 1 givenname: Benjamin orcidid: 0000-0002-9786-220X surname: Doerr fullname: Doerr, Benjamin email: doerr@lix.polytechnique.fr organization: Laboratoire d’Informatique, CNRS, École Polytechnique, Institut Polytechnique de Paris, Palaiseau, France – sequence: 2 givenname: Weijie orcidid: 0000-0002-8483-0161 surname: Zheng fullname: Zheng, Weijie email: zhengwj13@tsinghua.org.cn organization: Department of Computer Science and Technology, Tsinghua University, Beijing, China |
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| SubjectTerms | 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 |
| Title | Sharp Bounds for Genetic Drift in Estimation of Distribution Algorithms |
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