Neighborhood-Based Variable Ordering Heuristics for the Constraint Satisfaction Problem

  • Christian Bessiére
  • Assef Chmeiss
  • Lakhdar Saïs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2239)


One of the key factors in the efficiency of backtracking algorithms is the rule they use to decide on which variable to branch next (namely, the variable ordering heuristics). In this paper, we give a formulation of dynamic variable ordering heuristics that takes into account the properties of the neighborhood of the variable.


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  1. 1.
    C. Bessiére, A. Chmeiss, and L. Saïs. Neighborhood-based variable ordering heuristics for the constraint satisfaction problem. Technical Report 01002, LIRMM-University of Montpelllier II, Montpellier, France, January 2001. (available at
  2. 2.
    C. Bessiére and J.C. Régin. MAC and combined heuristics: two reasons to forsake FC (and CBJ?) on hard problems. In Proceedings CP’96, pages 61–75, Cambridge MA, 1996.Google Scholar
  3. 3.
    D. Brélaz. New methods to color the vertices of a graph. Communications of the ACM, 22:251–256, 1979.zbMATHCrossRefGoogle Scholar
  4. 4.
    R.M. Haralick and G.L. Elliott. Increasing tree seach efficiency for constraint satisfaction problems. Artificial Intelligence, 14:263–313, 1980.CrossRefGoogle Scholar
  5. 5.
    B. Smith and S.A. Grant. Trying harder to fail first. In Proceedings ECAI’98, pages 249–253, Brighton, UK, 1998.Google Scholar
  6. 6.
    B.M. Smith. The Brélaz heuristic and optimal static orderings. In Proceedings CP’99, pages 405–418, Alexandria VA, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Christian Bessiére
    • 1
  • Assef Chmeiss
    • 2
  • Lakhdar Saïs
    • 2
  1. 1.LIRMM-CNRS (UMR 5506)Member of the Coconut groupMontpellier Cedex 5France
  2. 2.CRILUniversité d’Artois - IUT de LensLENS CedexFrance

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