Document Detail


An information-theoretic analysis on the interactions of variables in combinatorial optimization problems.
MedLine Citation:
PMID:  17535138     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Dong-Il Seo; Byung-Ro Moon
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Evolutionary computation     Volume:  15     ISSN:  1063-6560     ISO Abbreviation:  Evol Comput     Publication Date:  2007  
Date Detail:
Created Date:  2007-05-30     Completed Date:  2007-07-20     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9513581     Medline TA:  Evol Comput     Country:  United States    
Other Details:
Languages:  eng     Pagination:  169-98     Citation Subset:  IM    
Affiliation:
School of Computer Science & Engineering, Seoul National University, Sillim-dong, Gwanak-gu, Seoul, 151-744 Korea. diseo@soar.snu.ac.kr
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MeSH Terms
Descriptor/Qualifier:
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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