Diversification and Determinism in Local Search for Satisfiability

  • Chu Min Li
  • Wen Qi Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3569)


The choice of the variable to flip in the Walksat family procedures is always random in that it is selected from a randomly chosen unsatisfied clause c. This choice in Novelty or R-Novelty heuristics also contains some determinism in that the variable to flip is always limited to the two best variables in c. In this paper, we first propose a diversification parameter for Novelty (or R-Novelty) heuristic to break the determinism in Novelty and show its performance compared with the random walk parameter in Novelty+. Then we exploit promising decreasing paths in a deterministic fashion in local search using a gradient-based approach. In other words, when promising decreasing paths exist, the variable to flip is no longer selected from a randomly chosen unsatisfied clause but in a deterministic fashion to surely decrease the number of unsatisfied clauses. Experimental results show that the proposed diversification and the determinism allow to significantly improve Novelty (and Walksat).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Chu Min Li
    • 1
  • Wen Qi Huang
    • 2
  1. 1.LaRIAUniversité de Picardie Jules VerneAmiens Cedex 1France
  2. 2.Huazhong university of science and technologyWuhanChina

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