Advances in Local Search for Satisfiability

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Author(s)
Pham, Duc Nghia
Thornton, John
Gretton, Charles
Sattar, Abdul
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Orgun, MA

Thornton, J

Date
2007
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253591 bytes

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Gold Coast, AUSTRALIA

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Abstract

In 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.

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AI 2007: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS

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4830

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© 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.

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