Analysis of Heuristic Synergies

  • Richard J. Wallace
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3978)


“Heuristic synergy” refers to improvements in search performance when the decisions made by two or more heuristics are combined. This paper considers combinations based on products and quotients, and a less familiar form of combination based on weighted sums of ratings from a set of base heuristics, some of which result in definite improvements in performance. Then, using recent results from a factor analytic study of heuristic performance, which had demonstrated two main effects of heuristics involving either buildup of contention or look-ahead-induced failure, it is shown that heuristic combinations are effective when they are able to balance these two actions. In addition to elucidating the basis for heuristic synergy (or lack thereof), this work suggests that the task of understanding heuristic search depends on the analysis of these two basic actions.


Ratio Node Constraint Satisfaction Problem Search Node Heuristic Performance Improve Search Performance 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bessière, C., Régin, J.-C.: Mac and combined heuristics: Two reasons to forsake fc (and cbj?) on hard problems. In: Freuder, E.C. (ed.) CP 1996. LNCS, vol. 1118, pp. 61–75. Springer, Heidelberg (1996)Google Scholar
  2. 2.
    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
  3. 3.
    Wallace, R.J.: Factor analytic studies of csp heuristics. In: van Beek, P. (ed.) CP 2005. LNCS, vol. 3709, pp. 712–726. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Harman, H.H.: Modern Factor Analysis, 2nd edn. University of Chicago, Chicago and London (1967)MATHGoogle Scholar
  5. 5.
    Lawley, D.N., Maxwell, A.E.: Factor Analysis as a Statistical Method, 2nd edn. Butterworths, London (1971)MATHGoogle Scholar
  6. 6.
    Smith, B.M., Grant, S.A.: Trying harder to fail first. In: Proc. Thirteenth European Conference on Artificial Intelligence-ECAI 1998, pp. 249–253. John Wiley & Sons, Chichester (1998)Google Scholar
  7. 7.
    Geelen, P.A.: Dual viewpoint heuristics for binary constraint satisfaction problems. In: Proc. Tenth European Conference on Artificial Intelligence-ECAI 1992, pp. 31–35 (1992)Google Scholar
  8. 8.
    Wallace, R.J.: Csp heuristics categorized with factor analytic. In: Creaney, N. (ed.) Proc. Sixteenth Irish Conference on Artificial Intelligence and Cognitive Science, Coleraine, NI, University of Ulster, pp. 213–222 (2005)Google Scholar
  9. 9.
    Beck, J.C., Prosser, P., Wallace, R.J.: Variable ordering heuristics show promise. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 711–715. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Beck, J.C., Prosser, P., Wallace, R.J.: Trying again to fail-first. In: Faltings, B.V., Petcu, A., Fages, F., Rossi, F. (eds.) CSCLP 2004. LNCS, vol. 3419, pp. 41–55. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Bessière, C., Zanuttini, B., Fernández, C.: Measuring search trees. In: ECAI 2004 Workshop on Modelling and Solving Problems with Constraints, pp. 31–40 (2004)Google Scholar
  12. 12.
    Boussemart, F., Hemery, F., Lecoutre, C., Sais, L.: Boosting systematic search by weighting constraints. In: Proc. Sixteenth European Conference on Artificial Intelligence-ECAI 2004, pp. 146–150 (2004)Google Scholar
  13. 13.
    Gent, I., MacIntyre, E., Prosser, P., Smith, B., Walsh, T.: An empirical study of dynamic variable ordering heuristics for the constraint satisfaction problem. In: Freuder, E.C. (ed.) CP 1996. LNCS, vol. 1118, pp. 179–193. Springer, Heidelberg (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Richard J. Wallace
    • 1
  1. 1.Cork Constraint Computation Centre and Department of Computer ScienceUniversity College CorkCorkIreland

Personalised recommendations