Abstract

Instance-specific algorithm configuration generalizes both instance-oblivious algorithm tuning as well as algorithm portfolio generation. ISAC is a recently proposed non-model-based approach for tuning solver parameters dependent on the specific instance that needs to be solved. While ISAC has been compared with instance-oblivious algorithm tuning systems before, to date a comparison with portfolio generators and other instance-specific algorithm configurators is crucially missing. In this paper, among others, we provide a comparison with SATzilla, as well as three other algorithm configurators: Hydra, DCM and ArgoSmart. Our experimental comparison shows that non-model-based ISAC significantly outperforms prior state-of-the-art algorithm selectors and configurators. The following study was the foundation for the best sequential portfolio at the 2011 SAT Competition.

Keywords

Algorithm Selection Test Instance Constraint Programming Training Instance Portfolio Approach 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adenso-Diaz, B., Laguna, M.: Fine-tuning of Algorithms using Fractional Experimental Design and Local Search. Operations Research 54(1), 99–114 (2006)MATHCrossRefGoogle Scholar
  2. 2.
    Ansótegui, C., Sellmann, M., Tierney, K.: A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 142–157. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Biere, A.: Picosat version 535. Solver description. SAT Competition (2007)Google Scholar
  5. 5.
    Bregman, D.R., Mitchell, D.G.: The SAT Solver MXC, version 0.75. Solver description. SAT Race Competition (2008)Google Scholar
  6. 6.
    Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996)MathSciNetMATHGoogle Scholar
  7. 7.
    Dequen, G., Dubois, O.: Dubois. kcnfs. Solver description. SAT Competition (2007)Google Scholar
  8. 8.
    Gomes, C.P., Selman, B.: Algorithm Portfolios. Artificial Intelligence 126(1-2), 43–62 (2001)MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Hamerly, G., Elkan, C.: Learning the K in K-Means. In: NIPS (2003)Google Scholar
  10. 10.
    Heule, M., Dufour, M., van Zwieten, J.E., van Maaren, H.: March_eq: Implementing Additional Reasoning into an Efficient Look-Ahead SAT Solver. In: H. Hoos, H., Mitchell, D.G. (eds.) SAT 2004. LNCS, vol. 3542, pp. 345–359. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Hoos, H.H.: Adaptive Novelty+: Novelty+ with adaptive noise. In: AAAI (2002)Google Scholar
  12. 12.
    Hutter, F., Tompkins, D., Hoos, H.H.: RSAPS: Reactive Scaling And Probabilistic Smoothing. In: CP (2002)Google Scholar
  13. 13.
    Hutter, F., Hamadi, Y.: Parameter Adjustment Based on Performance Prediction: Towards an Instance-Aware Problem Solver. Technical Report, MSR-TR-2005-125, Microsoft Research Cambridge (2005)Google Scholar
  14. 14.
    Hutter, F., Hamadi, Y., Hoos, H.H., Leyton-Brown, K.: Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms. In: Benhamou, F. (ed.) CP 2006. LNCS, vol. 4204, pp. 213–228. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Hutter, F., Hoos, H.H., Leyton-Brown, K., Stuetzle, T.: ParamILS: An Automatic Algorithm Configuration Framework. JAIR 36, 267–306 (2009)MATHGoogle Scholar
  16. 16.
    Kadioglu, S., Malitsky, Y., Sellmann, M., Tierney, K.: ISAC – Instance-Specific Algorithm Configuration. In: ECAI, pp. 751–756 (2010)Google Scholar
  17. 17.
    KhudaBukhsh, A.R., Xu, L., Hoos, H.H., Leyton-Brown, K.: SATenstein: Automatically Building Local Search SAT Solvers From Components. In: IJCAI (2009)Google Scholar
  18. 18.
    Leyton-Brown, K., Nudelman, E., Andrew, G., McFadden, J., Shoham, Y.: A Portfolio Approach to Algorithm Selection. In: IJCAI, pp. 1542–1543 (2003)Google Scholar
  19. 19.
    Li, C.M., Huang, W.Q.: G2WSAT: Gradient-based Greedy WalkSAT. In: Bacchus, F., Walsh, T. (eds.) SAT 2005. LNCS, vol. 3569, pp. 158–172. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  20. 20.
    Nikolić, M., Marić, F., Janičić, P.: Instance-Based Selection of Policies for SAT Solvers. In: Kullmann, O. (ed.) SAT 2009. LNCS, vol. 5584, pp. 326–340. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  21. 21.
    O’Mahony, E., Hebrard, E., Holland, A., Nugent, C., O’Sullivan, B.: Using Case-based Reasoning in an Algorithm Portfolio for Constraint Solving. In: Irish Conference on Artificial Intelligence and Cognitive Science (2008)Google Scholar
  22. 22.
    Pham, D.N., Anbulagan: ranov. Solver description. SAT Competition (2007)Google Scholar
  23. 23.
    Pham, D.N., Gretton, C.: gnovelty+. Solver description. SAT Competition (2007)Google Scholar
  24. 24.
    Prestwich, S.: VW: Variable Weighting Scheme. In: SAT (2005)Google Scholar
  25. 25.
  26. 26.
    Smith-Miles, K.A.: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. 41(1), 6:1–6:25 (2009)CrossRefGoogle Scholar
  27. 27.
    Silverthorn, B., Miikkulainen, R.: Latent Class Models for Algorithm Portfolio Methods. In: AAAI (2010)Google Scholar
  28. 28.
    Thornton, J., Pham, D.N., Bain, S., Ferreira, V.: Additive versus multiplicative clause weighting for SAT. In: PRICAI, pp. 405–416 (2008)Google Scholar
  29. 29.
    Tompkins, D.A.D., Hutter, F., Hoos, H.H.: saps. Solver description. SAT Competition (2007)Google Scholar
  30. 30.
    Wei, W., Li, C.M., Zhang, H.: Combining adaptive noise and promising decreasing variables in local search for SAT. Solver description. SAT Competition (2007)Google Scholar
  31. 31.
    Wei, W., Li, C.M., Zhang, H.: Deterministic and random selection of variables in local search for SAT. Solver description. SAT Competition (2007)Google Scholar
  32. 32.
    Wei, W., Li, C.M., Zhang, H.: adaptg2wsatp. Solver description. SAT Competition (2007)Google Scholar
  33. 33.
    Xu, L., Hoos, H.H., Leyton-Brown, K.: Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection. In: AAAI (2010)Google Scholar
  34. 34.
    Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: SATzilla2009: an Automatic Algorithm Portfolio for SAT. Solver description. SAT Competition (2009)Google Scholar
  35. 35.
    Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: SATzilla: Portfolio-based Algorithm Selection for SAT. JAIR 32(1), 565–606 (2008)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yuri Malitsky
    • 1
  • Meinolf Sellmann
    • 2
  1. 1.Department of Computer ScienceBrown UniversityUSA
  2. 2.IBM Research WatsonUSA

Personalised recommendations