| An information-theoretic analysis on the interactions of variables in combinatorial optimization problems. | |
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MedLine Citation:
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PMID: 17535138 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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In optimization problems, the contribution of a variable to fitness often depends on the states of other variables. This phenomenon is referred to as epistasis or linkage. In this paper, we show that a new theory of epistasis can be established on the basis of Shannon's information theory. From this, we derive a new epistasis measure called entropic epistasis and some theoretical results. We also provide experimental results verifying the measure and showing how it can be used for designing efficient evolutionary algorithms. |
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Authors:
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Dong-Il Seo; Byung-Ro Moon |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: Evolutionary computation Volume: 15 ISSN: 1063-6560 ISO Abbreviation: Evol Comput Publication Date: 2007 |
Date Detail:
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Created Date: 2007-05-30 Completed Date: 2007-07-20 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9513581 Medline TA: Evol Comput Country: United States |
Other Details:
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Languages: eng Pagination: 169-98 Citation Subset: IM |
Affiliation:
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School of Computer Science & Engineering, Seoul National University, Sillim-dong, Gwanak-gu, Seoul, 151-744 Korea. diseo@soar.snu.ac.kr |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Computational Biology* Epistasis, Genetic Evolution* Information Theory Models, Genetic Models, Statistical |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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