| On the effect of populations in evolutionary multi-objective optimisation. | |
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MedLine Citation:
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PMID: 20560762 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Multi-objective evolutionary algorithms (MOEAs) have become increasingly popular as multi-objective problem solving techniques. An important open problem is to understand the role of populations in MOEAs. We present two simple bi-objective problems which emphasise when populations are needed. Rigorous runtime analysis points out an exponential runtime gap between the population-based algorithm simple evolutionary multi-objective optimiser (SEMO) and several single individual-based algorithms on this problem. This means that among the algorithms considered, only the population-based MOEA is successful and all other algorithms fail. |
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Authors:
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Oliver Giel; Per Kristian Lehre |
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: 18 ISSN: 1530-9304 ISO Abbreviation: Evol Comput Publication Date: 2010 |
Date Detail:
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Created Date: 2010-08-03 Completed Date: 2010-11-05 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: 335-56 Citation Subset: IM |
Affiliation:
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Fakultät für Informatik, Technische Universität Dortmund, Germany. Oliver.Giel@cs.uni-dortmund.de |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Evolution* Genetics, Population Models, Statistical Population Dynamics |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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