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A New Method for Modeling the Behavior of Finite Population Evolutionary Algorithms
http://hdl.handle.net/10191/21942
http://hdl.handle.net/10191/2194280b6e6cf-d6ce-4cea-acb4-a182c477bc19
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18_3_451-489.pdf (504.8 kB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2013-04-19 | |||||
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言語 | en | |||||
タイトル | A New Method for Modeling the Behavior of Finite Population Evolutionary Algorithms | |||||
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言語 | eng | |||||
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資源 | http://purl.org/coar/resource_type/c_6501 | |||||
タイプ | journal article | |||||
著者 |
Motoki, Tatsuya
× Motoki, Tatsuya |
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内容記述タイプ | Abstract | |||||
内容記述 | As practitioners we are interested in the likelihood of the population containing a copy of the optimum. The dynamic systems approach, however, does not help us to calculate that quantity. Markov chain analysis can be used in principle to calculate the quantity. However, since the associated transition matrices are enormous even for modest problems, it follows that in practice these calculations are usually computationally infeasible. Therefore, some improvements on this situation are desirable. In this paper, we present a method for modeling the behavior of finite population evolutionary algorithms (EAs), and show that if the population size is greater than 1 and much less than the cardinality of the search space, the resulting exact model requires considerably less memory space for theoretically running the stochastic search process of the original EA than the Nix and Vose-style Markov chain model. We also present some approximate models that use still less memory space than the exact model. Furthermore, based on our models, we examine the selection pressure by fitness-proportionate selection, and observe that on average over all population trajectories, there is no such strong bias toward selecting the higher fitness individuals as the fitness landscape suggests. | |||||
書誌情報 |
Evolutionary Computation en : Evolutionary Computation 巻 18, 号 3, p. 451-489, 発行日 2010-09 |
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出版者 | MIT Press | |||||
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収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1063-6560 | |||||
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収録物識別子タイプ | NCID | |||||
収録物識別子 | AA10913479 | |||||
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識別子タイプ | DOI | |||||
関連識別子 | http://doi.org/10.1162/evco_a_00001 | |||||
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出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
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値 | publisher |