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Abstract

Local search algorithms are one of the effective methods for solving hard combinatorial problems. However, a serious problem of this approach is that the search often traps at local optima. At AAAI 2004, Fang and Ruml proposed a novel approach which makes local optima disappeared. The basic idea is that, at each local optimal point during the search, the value of the objective function (a local gradient function) at that point is changed by adding some information into the database. Once no more local optima exist, the local search can always find a global optimal. In this paper, along the same approach of Fang and Ruml, we propose a different objective function based on an ordering of propositional variables. Based on this ordering, ordered resolution is performed at each local optimal point and the resolvent is added into the database. This resolvent always increases the value of the objective function so that the local optimal point disappears after a finite number of steps. Preliminary experimental results show that our method and Fang and Ruml’s method have better performances in different areas.

Keywords

Objective Function Local Search Local Search Algorithm Preliminary Experimental Result Current Assignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Haiou Shen
    • 1
  • Hantao Zhang
    • 1
  1. 1.Department of Computer ScienceUniversity of IowaIowa CityU.S.A.

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