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dc.contributor.authorPham, Duc Nghia
dc.contributor.authorThornton, John
dc.contributor.authorGretton, Charles
dc.contributor.authorSattar, Abdul
dc.contributor.editorHans van Maaren (Editor-in-Chief)
dc.date.accessioned2017-05-03T12:54:35Z
dc.date.available2017-05-03T12:54:35Z
dc.date.issued2008
dc.date.modified2013-07-12T01:38:00Z
dc.identifier.issn15740617
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherIOS Press
dc.publisher.placeNetherlands
dc.publisher.urihttp://www.iospress.nl/journal/journal-on-satisfiability-boolean-modeling-and-computation/
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom149
dc.relation.ispartofpageto172
dc.relation.ispartofjournalJournal on Satisfiability, Boolean Modeling and Computation
dc.relation.ispartofvolume4
dc.rights.retentionY
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchComputation Theory and Mathematics
dc.subject.fieldofresearchcode080199
dc.subject.fieldofresearchcode0802
dc.titleCombining Adaptive and Dynamic Local Search for Satisfiability
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
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.
gro.date.issued2008
gro.hasfulltextNo Full Text
gro.griffith.authorSattar, Abdul
gro.griffith.authorThornton, John R.


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