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dc.contributor.authorGretton, Charlesen_US
dc.contributor.authorPham, Duc Nghiaen_US
dc.contributor.authorSattar, Abdulen_US
dc.contributor.authorThornton, Johnen_US
dc.contributor.editorHans van Maaren (Editor-in-Chief)en_US
dc.date.accessioned2017-04-04T16:51:23Z
dc.date.available2017-04-04T16:51:23Z
dc.date.issued2008en_US
dc.date.modified2013-07-12T01:38:00Z
dc.identifier.issn15740617en_US
dc.identifier.urihttp://hdl.handle.net/10072/23564
dc.description.abstractIn this paper we describe a stochastic local search (SLS) procedure for finding models of satisfiable propositional formulae. This new algorithm, gNovelty+, draws on the features of two other WalkSAT family algorithms: AdaptNovelty+ and G2WSAT, while also successfully employing a hybrid clause weighting heuristic based on the features of two dynamic local search (DLS) algorithms: PAWS and (R)SAPS. gNovelty+ was a Gold Medal winner in the random category of the 2007 SAT competition. In this paper we present a detailed description of the algorithm and extend the SAT competition results via an empirical study of the effects of problem structure, parameter tuning and resolution preprocessors on the performance of gNovelty+. The study compares gNovelty+ with three of the most representativeWalkSAT-based solvers: AdaptG2WSAT0, G2WSAT and AdaptNovelty+, and two of the most representative DLS solvers: RSAPS and PAWS. Our new results augment the SAT competition results and show that gNovelty+ is also highly competitive in the domain of solving structured satisfiability problems in comparison with other SLS techniques.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherIOS Pressen_US
dc.publisher.placeNetherlandsen_US
dc.publisher.urihttp://www.iospress.nl/journal/journal-on-satisfiability-boolean-modeling-and-computation/en_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom149en_US
dc.relation.ispartofpageto172en_US
dc.relation.ispartofjournalJournal on Satisfiability, Boolean Modeling and Computationen_US
dc.relation.ispartofvolume4en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classifieden_US
dc.subject.fieldofresearchcode080199en_US
dc.titleCombining Adaptive and Dynamic Local Search for Satisfiabilityen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.rights.copyrightSelf-archiving of the author-manuscript version is not yet supported by this journal. Please refer to the journal link for access to the definitive, published version or contact the author[s] for more information.en_US
gro.date.issued2008
gro.hasfulltextNo Full Text


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