Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/3404
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dc.contributor.authorPrestwich S.D.-
dc.contributor.authorTarim S.A.-
dc.contributor.authorRossi R.-
dc.contributor.authorHnich B.-
dc.date.accessioned2023-06-16T14:58:02Z-
dc.date.available2023-06-16T14:58:02Z-
dc.date.issued2010-
dc.identifier.isbn3642135196-
dc.identifier.isbn9783642135194-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://doi.org/10.1007/978-3-642-13520-0_30-
dc.identifier.urihttps://hdl.handle.net/20.500.14365/3404-
dc.descriptionThe ARTIST Design;Network of Excellence;The Institute for Computational Sustainability (ICS);The Cork Constraint Computation Center;The Association for Constraint Programming (ACP)en_US
dc.description7th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2010 -- 14 June 2010 through 18 June 2010 -- Bologna -- 81368en_US
dc.description.abstractStochastic Constraint Programming is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. A solution to such a problem is a policy tree that specifies decision variable assignments in each scenario. Several complete solution methods have been proposed, but the authors recently showed that an incomplete approach based on neuroevolution is more scalable. In this paper we hybridise neuroevolution with constraint filtering on hard constraints, and show both theoretically and empirically that the hybrid can learn more complex policies more quickly. © 2010 Springer-Verlag.en_US
dc.description.sponsorshipSOBAG-108K027; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK; Hacettepe Üniversitesien_US
dc.description.sponsorshipS. A. Tarim and B. Hnich are supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant No. SOBAG-108K027. S. A. Tarim is also supported by Hacettepe University (BAB). A version of this algorithm will used to further research in risk management as part of a collaboration with IBM Research, with partial support from the Irish Development Association and IRCSET.en_US
dc.language.isoenen_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCombinatorial problemen_US
dc.subjectComplete solutionsen_US
dc.subjectConstraint programmingen_US
dc.subjectDecision variablesen_US
dc.subjectHard constraintsen_US
dc.subjectNeuroevolutionen_US
dc.subjectStochastic constraintsen_US
dc.subjectCombinatorial optimizationen_US
dc.subjectComputer programmingen_US
dc.subjectConstraint theoryen_US
dc.subjectDecision makingen_US
dc.subjectStochastic systemsen_US
dc.subjectProblem solvingen_US
dc.titleStochastic Constraint Programming by Neuroevolution With Filteringen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1007/978-3-642-13520-0_30-
dc.identifier.scopus2-s2.0-77955452287-
dc.authorscopusid7004234709-
dc.authorscopusid35563636800-
dc.authorscopusid6602458958-
dc.identifier.volume6140 LNCSen_US
dc.identifier.startpage282en_US
dc.identifier.endpage286en_US
dc.identifier.wosWOS:000279617200030-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ3-
dc.identifier.wosqualityN/A-
item.openairetypeConference Object-
item.grantfulltextopen-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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