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Analysis of Heuristic Synergies

  • Conference paper
Recent Advances in Constraints (CSCLP 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3978))

Abstract

“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.

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References

  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. 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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  4. Harman, H.H.: Modern Factor Analysis, 2nd edn. University of Chicago, Chicago and London (1967)

    MATH  Google Scholar 

  5. Lawley, D.N., Maxwell, A.E.: Factor Analysis as a Statistical Method, 2nd edn. Butterworths, London (1971)

    MATH  Google Scholar 

  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. 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. 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. 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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. 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 

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Author information

Authors and Affiliations

  1. Cork Constraint Computation Centre and Department of Computer Science, University College Cork, Cork, Ireland

    Richard J. Wallace

Authors
  1. Richard J. Wallace
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Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Izmir University of Economics, Turkey

    Brahim Hnich

  2. SICS, P.O. Box 1263, SE-164 29, Kista, Sweden

    Mats Carlsson

  3. Projet Contraintes, INRIA Rocquencourt,, Domaine de Voluceau, BP105, 78153, Le Chesnay Cedex, France

    François Fages

  4. Dipartimento di Matematica Pura ed Applicata, Università di Padova, Via Trieste 63, 35121, Padova, Italy

    Francesca Rossi

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© 2006 Springer-Verlag Berlin Heidelberg

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Wallace, R.J. (2006). Analysis of Heuristic Synergies. In: Hnich, B., Carlsson, M., Fages, F., Rossi, F. (eds) Recent Advances in Constraints. CSCLP 2005. Lecture Notes in Computer Science(), vol 3978. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11754602_6

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  • DOI: https://doi.org/10.1007/11754602_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34215-1

  • Online ISBN: 978-3-540-34216-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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