Advances in Local Search for Satisfiability
نویسندگان
چکیده
In this paper we describe a stochastic local search (SLS) procedure for finding satisfying models of satisfiable propositional formulae. This new algorithm, gNovelty, draws on the features of two other WalkSAT family algorithms: R+AdaptNovelty and GWSAT, 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 performance of gNovelty. The study also compares gNovelty with two of the most representative WalkSAT-based solvers: GWSAT, 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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