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dc.contributor.authorPham, Nghiaen_US
dc.contributor.authorThornton, Johnen_US
dc.contributor.authorGretton, Charlesen_US
dc.contributor.authorSattar, Abdulen_US
dc.contributor.editorMehmet A. Orgun, John Thorntonen_US
dc.date.accessioned2017-05-03T12:54:32Z
dc.date.available2017-05-03T12:54:32Z
dc.date.issued2007en_US
dc.date.modified2008-11-19T02:56:26Z
dc.identifier.refurihttp://www.cit.gu.edu.au/conferences/austai/en_AU
dc.identifier.doi10.1007/978-3-540-76928-6_23en_US
dc.identifier.urihttp://hdl.handle.net/10072/18345
dc.description.abstractIn this paper we describe a stochastic local search (SLS) procedure for finding satisfying models of satisfiable propositional for- mulae. This new algorithm, gNovelty+, draws on the features of two other WalkSAT family algorithms: R+AdaptNovelty+ and G2WSAT, while also successfully employing a dynamic local search (DLS) clause weighting heuristic to further improve performance. 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 and parameter tuning on the perfor- mance of gNovelty+. The study also compares gNovelty+ with two of the most representative WalkSAT-based solvers: G2WSAT, 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_AU
dc.format.extent253591 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringeren_US
dc.publisher.placeHeidelberg, Germanyen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencenameAustralian Joint Conference on Artificial Intelligenceen_US
dc.relation.ispartofconferencetitleAI 2007: Advances in Artificial Intelligenceen_US
dc.relation.ispartofdatefrom2007-12-02en_US
dc.relation.ispartofdateto2007-12-06en_US
dc.relation.ispartoflocationGold Coast, Australiaen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280213en_US
dc.titleAdvances in Local Search for Satisfiabilityen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2007 Springer. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com.en_AU
gro.date.issued2007
gro.hasfulltextFull Text


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    Contains papers delivered by Griffith authors at national and international conferences.

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