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dc.contributor.authorCai, Shaoweien_US
dc.contributor.authorSu, Kaileen_US
dc.contributor.editorTaghi M. Khoshgoftaar and Xingquan (Hill) Zhuen_US
dc.description.abstractAbstract-Local Search is an appealing method for solving the Boolean Satisfiability problem (SAT). However, this method suffers from the cycling problem which severely limits its power. Recently, a new strategy called configuration checking (CC) was proposed, for handling the cycling problem in local search. The CC strategy was used to improve a state-ofthe- art local search algorithm for Minimum Vertex Cover. In this paper, we propose a novel local search strategy for the satisfiability problem, i.e., the CC strategy for SAT. The CC strategy for SAT takes into account the circumstances of the variables when selecting a variable to flip, where the circumstance of a variable refers to truth values of all its neighboring variables. We then apply it to design a local search algorithm for SAT called SWcc (Smoothed Weighting with Configuration Checking). Experimental results show that the CC strategy for SAT is more efficient than the previous strategy for handling the cycling problem called tabu. Moreover, SWcc significantly outperforms the best local search SAT solver in SAT Competition 2009 called TNM on large random 3-SAT instances.en_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofconferencenameICTAI 2011en_US
dc.relation.ispartofconferencetitleProceedings of 2011 23rd IEEE International Conference on Tools with Artificial Intelligence ICTAI 2011en_US
dc.relation.ispartoflocationBoca Raton, Florida, United Statesen_US
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classifieden_US
dc.titleLocal Search with Configuration Checking for SATen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.hasfulltextNo Full Text

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