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Combining Adaptive Noise and Look-Ahead in Local Search for SAT

  • Chu Min Li
  • Wanxia Wei
  • Harry Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4501)

Abstract

The adaptive noise mechanism was introduced in Novelty+ to automatically adapt noise settings during the search [4]. The local search algorithm G 2 WSAT 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 G 2 WSAT to obtain an algorithm adaptG 2 WSAT, 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 adaptG 2 WSAT, resulting in a new local search algorithm called adaptG 2 WSAT P . Without any manual noise or other parameter tuning, adaptG 2 WSAT P shows generally good performance, compared with G 2 WSAT with approximately optimal static noise settings, or is sometimes even better than G 2 WSAT. In addition, adaptG 2 WSAT P is favorably compared with state-of-the-art local search algorithms such as R+adaptNovelty+ and VW.

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References

  1. 1.
    Anbulagan, A., et al.: Old Resolution Meets Modern SLS. In: Proceedings of AAAI-2005, pp. 354–359. AAAI Press, Menlo Park (2005)Google Scholar
  2. 2.
    Gent, I.P., Walsh, T.: Towards an Understanding of Hill-Climbing Procedures for SAT. In: Proceedings of AAAI-1993, pp. 28–33. AAAI Press, Menlo Park (1993)Google Scholar
  3. 3.
    Hoos, H.: On the Run-Time Behavior of Stochastic Local Search Algorithms for SAT. In: Proceedings of AAAI-1999, pp. 661–666. AAAI Press, Menlo Park (1999)Google Scholar
  4. 4.
    Hoos, H.: An Adaptive Noise Mechanism for WalkSAT. In: Proceedings of AAAI-2002, pp. 655–660. AAAI Press, Menlo Park (2002)Google Scholar
  5. 5.
    Li, C.M., Huang, W.Q.: Diversification and Determinism in Local Search for Satisfiability. In: Bacchus, F., Walsh, T. (eds.) SAT 2005. LNCS, vol. 3569, pp. 158–172. Springer, Heidelberg (2005)Google Scholar
  6. 6.
    Li, C.M., Wei, W., Zhang, H.: Combining Adaptive Noise and Look-Ahead in Local Search for SAT. In: Proceedings of LSCS-2006, pp. 2–16 (2006)Google Scholar
  7. 7.
    Li, C.M., Wei, W., Zhang, H.: Combining Adaptive Noise and Look-Ahead in Local Search for SAT. In: Benhamou, F., Jussien, N., O’Sullivan, B. (eds.) Trends in Constraint Programming. Hermes Science, Paris (to appear) (2007)Google Scholar
  8. 8.
    Mazure, B., Sais, L., Gregoire, E.: Tabu Search for SAT. In: Proceedings of AAAI-1997, pp. 281–285. AAAI Press, Menlo Park (1997)Google Scholar
  9. 9.
    McAllester, D.A., Selman, B., Kautz, H.: Evidence for Invariant in Local Search. In: Proceedings of AAAI-1997, pp. 321–326. AAAI Press, Menlo Park (1997)Google Scholar
  10. 10.
    Patterson, D.J., Kautz, H.: Auto-Walksat: A Self-Tuning Implementation of Walksat. Electronic Notes on Discrete Mathematics 9 (2001)Google Scholar
  11. 11.
    Prestwich, S.: Random Walk with Continuously Smoothed Variable Weights. In: Bacchus, F., Walsh, T. (eds.) SAT 2005. LNCS, vol. 3569, pp. 203–215. Springer, Heidelberg (2005)Google Scholar
  12. 12.
    Schuurmans, D., Southey, F.: Local Search Characteristics of Incomplete SAT Procedures. In: Proceedings of AAAI-2000, pp. 297–302. AAAI Press, Menlo Park (2000)Google Scholar
  13. 13.
    Tompkins, D.A.D., Hoos, H.H.: UBCSAT: An Implementation and Experimentation Environment for SLS Algorithms for SAT and MAX-SAT. In: Hoos, H.H., Mitchell, D.G. (eds.) SAT 2004. LNCS, vol. 3542, pp. 306–315. Springer, Heidelberg (2005)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Chu Min Li
    • 1
  • Wanxia Wei
    • 2
  • Harry Zhang
    • 2
  1. 1.LaRIA, Université de Picardie Jules Verne, 33 Rue St. Leu, 80039 Amiens Cedex 01France
  2. 2.Faculty of Computer Science, University of New Brunswick, Fredericton, NB, E3B 5A3Canada

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