Combining adaptive noise and look-ahead in local search for SAT

  • Authors:

  • Chu Min Li;Wanxia Wei;Harry Zhang

  • Affiliations:

  • LaRIA, Université de Picardie Jules Verne, Amiens Cedex 01, France;Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada;Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada

  • Venue:
  • SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
  • Year:
  • 2007

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Abstract

The adaptive noise mechanism was introduced in Novelty+ to automatically adapt noise settings during the search [4]. The local search algorithm G2WSAT deterministically exploits promising decreasing variables to reduce randomness and consequently the dependence on noise parameters. In this paper, we first integrate the adaptive noise mechanism in G2WSAT to obtain an algorithm adaptG2WSAT, whose performance suggests that the deterministic exploitation of promising decreasing variables cooperates well with this mechanism. Then, we propose an approach that uses look-ahead for promising decreasing variables to further reinforce this cooperation. We implement this approach in adaptG2WSAT, resulting in a new local search algorithm called adaptG2WSATP. Without any manual noise or other parameter tuning, adaptG2WSATP shows generally good performance, compared with G2WSAT with approximately optimal static noise settings, or is sometimes even better than G2WSAT. In addition, adaptG2WSATP is favorably compared with state-of-the-art local search algorithms such as R+adaptNovelty+ and VW.