Efficient and Experimental Meta-heuristics for MAX-SAT Problems

  • Dalila Boughaci
  • Habiba Drias
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3503)


Many problems in combinatorial optimization are NP-Hard. This has forced researchers to explore meta-heuristic techniques for dealing with this class of complex problems and finding an acceptable solution in reasonable time. The satisfiability problem, SAT, is studied by a great number of researchers the three last decades. Its wide application to the domain of AI in automatic reasoning and problem solving for instance and other domains like VLSI and graph theory motivates the huge interest shown for this problem. In this paper, tabu search, scatter search, genetic algorithms and memetic evolutionary meta-heuristics are studied for the NP-Complete satisfiability problems, in particular for its optimization version namely MAX-SAT. Experiments comparing the proposed approaches for solving MAX-SAT problems are represented. The empirical tests are performed on DIMACS benchmark instances.


Genetic Algorithm Local Search Tabu Search Memetic Algorithm Conjunctive Normal Form 
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

  • Dalila Boughaci
    • 1
  • Habiba Drias
    • 2
  1. 1.ITSUniversity of sciences and technologyAlgiersAlgeria
  2. 2.National Institute of Computer ScienceAlger

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