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An Evolutionary Local Search Algorithm for the Satisfiability Problem

  • Levent Aksoy
  • Ece Olcay Gunes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3949)

Abstract

Satisfiability problem is an NP-complete problem that finds itself or its variants in many combinatorial problems. There exist many complete algorithms that give successful results on hard problems, but they may be time-consuming because of their branch and bound structures. In this manner, many successful incomplete algorithms are introduced. In this paper, the improvement of incomplete algorithms is of interest and it is shown that the incomplete algorithms can be more efficient if they are equipped with the problem specific knowledge, goal-oriented operators, and knowledge-based methods. In this aspect, an evolutionary local search algorithm is implemented, tested on a randomly generated benchmark that includes test instances with different sizes, and compared with prominent incomplete algorithms. Also, effects of goal-oriented genetic operators and knowledge-based methods used in the evolution-ary local search algorithm are examined by making comparisons with blind operators and random methods.

Keywords

Local Search Test Instance Conjunctive Normal Form Local Search Method Satisfying 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 2006

Authors and Affiliations

  • Levent Aksoy
    • 1
  • Ece Olcay Gunes
    • 1
  1. 1.Faculty of Electrical and Electronics Engineering Department of Electronics and Communication EngineeringIstanbul Technical UniversityMaslak, IstanbulTurkey

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