Abstract
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 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 performance 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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