Evolving Variable-Ordering Heuristics for Constrained Optimisation

  • Stuart Bain
  • John Thornton
  • Abdul Sattar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3709)


In this paper we present and evaluate an evolutionary approach for learning new constraint satisfaction algorithms, specifically for MAX-SAT optimisation problems. Our approach offers two significant advantages over existing methods: it allows the evolution of more complex combinations of heuristics, and; it can identify fruitful synergies among heuristics. Using four different classes of MAX-SAT problems, we experimentally demonstrate that algorithms evolved with this method exhibit superior performance in comparison to general purpose methods.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Borchers, B., Furman, J.: A two-phase exact algorithm for MAX-SAT and weighted MAX-SAT problems. Journal of Combinatorial Optimization 2, 299–306 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Xing, Z., Zhang, W.: Efficient strategies for (weighted) maximum satisfiability. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 690–705. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1, 67–82 (1997)CrossRefGoogle Scholar
  4. 4.
    Minton, S.: Automatically configuring constraint satisfaction programs: A case study. Constraints 1, 7–43 (1996)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Epstein, S.L., Freuder, E.C., Wallace, R., Morozov, A., Samuels, B.: The adaptive constraint engine. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 525–540. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Fukunaga, A.: Automated discovery of composite SAT variable-selection heuristics. In: AAAI 2002, Canada, pp. 641–648 (2002)Google Scholar
  7. 7.
    Koza, J.: Genetic Programming: On the programming of computers by means of natural selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  8. 8.
    Zhang, H., Stickel, M.: Implementing the Davis-Putnam method. Journal of Automated Reasoning 24, 277–296 (2000)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Stuart Bain
    • 1
  • John Thornton
    • 1
  • Abdul Sattar
    • 1
  1. 1.Institute for Integrated and Intelligent SystemsGriffith UniversityAustralia

Personalised recommendations