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Dependency structure matrix, genetic algorithms, and effective recombination

2009-12Journal article Open access
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dc.contributor.authorYu, Tian-Li
dc.contributor.authorGoldberg, David E.
dc.contributor.authorSastry, Kumara
dc.contributor.authorLima, Claudio F.
dc.contributor.authorPelikan, Martin
dc.date.accessioned2018-12-07T14:58:19Z
dc.date.available2018-12-07T14:58:19Z
dc.date.issued2009-12
dc.description.abstractIn many different fields, researchers are often confronted by problems arising from complex systems. Simple heuristics or even enumeration works quite well on small and easy problems; however, to efficiently solve large and difficult problems, proper decomposition is the key. In this paper, investigating and analyzing interactions between components of complex systems shed some light on problem decomposition. By recognizing three bare-bones interactions-modularity, hierarchy, and overlap, facet-wise models arc developed to dissect and inspect problem decomposition in the context of genetic algorithms. The proposed genetic algorithm design utilizes a matrix representation of an interaction graph to analyze and explicitly decompose the problem. The results from this paper should benefit research both technically and scientifically. Technically, this paper develops an automated dependency structure matrix clustering technique and utilizes it to design a model-building genetic algorithm that learns and delivers the problem structure. Scientifically, the explicit interaction model describes the problem structure very well and helps researchers gain important insights through the explicitness of the procedure.
dc.description.sponsorshipThis work was sponsored by Taiwan National Science Council under grant NSC97- 2218-E-002-020-MY3, U.S. Air Force Office of Scientific Research, Air Force Material Command, USAF, under grants FA9550-06-1-0370 and FA9550-06-1-0096, U.S. National Science Foundation under CAREER grant ECS-0547013, ITR grant DMR-03-25939 at Materials Computation Center, grant ISS-02-09199 at US National Center for Supercomputing Applications, UIUC, and the Portuguese Foundation for Science and Technology under grants SFRH/BD/16980/2004 and PTDC/EIA/67776/2006.
dc.identifier.doihttps://doi.org/10.1162/evco.2009.17.4.17409
dc.identifier.issn1063-6560
dc.identifier.urihttp://hdl.handle.net/10400.1/11961
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMassachusetts Institute of Technology Press
dc.relationEfficiency enhancement techniques for probabilistic model building genetic algorithms
dc.subjectProduct development
dc.subjectInformation
dc.subjectPrinciple
dc.titleDependency structure matrix, genetic algorithms, and effective recombination
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleEfficiency enhancement techniques for probabilistic model building genetic algorithms
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBD%2F16980%2F2004/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FEIA%2F67776%2F2006/PT
oaire.citation.endPageU3
oaire.citation.issue4
oaire.citation.startPage595
oaire.citation.titleEvolutionary Computation
oaire.citation.volume17
oaire.fundingStreamSFRH
oaire.fundingStream3599-PPCDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
rcaap.typearticle
relation.isProjectOfPublicationc43e5eee-fd6a-45c5-825c-32b735c18289
relation.isProjectOfPublication22c6bdd9-deae-4b23-8132-7d9e0e4e52b1
relation.isProjectOfPublication.latestForDiscovery22c6bdd9-deae-4b23-8132-7d9e0e4e52b1

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