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MAC and combined heuristics: Two reasons to forsake FC (and CBJ?) on hard problems

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Part of the Lecture Notes in Computer Science book series (LNCS, volume 1118)

Abstract

In the last twenty years, many algorithms and heuristics were developed to find solutions in constraint networks. Their number increased to such an extent that it quickly became necessary to compare their performances in order to propose a small number of “good” methods. These comparisons often led us to consider FC or FC-CBJ associated with a “minimum domain” variable ordering heuristic as the best techniques to solve a wide variety of constraint networks.

In this paper, we first try to convince once and for all the CSP community that MAC is not only more efficient than FC to solve large practical problems, but it is also really more efficient than FC on hard and large random problems. Afterwards, we introduce an original and efficient way to combine variable ordering heuristics. Finally, we conjecture that when a good variable ordering heuristic is used, CBJ becomes an expensive gadget which almost always slows down the search, even if it saves a few constraint checks.

Keywords

Domain Size Constraint Satisfaction Hard Problem Constraint Satisfaction Problem Constraint Network 
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 1996

Authors and Affiliations

  1. 1.LIRMM (UMR 5506 CNRS)Montpellier cedex 5France
  2. 2.ILOG S.A.Gentilly CedexFrance

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