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Sequential Model-Based Optimization for General Algorithm Configuration

  • Frank Hutter
  • Holger H. Hoos
  • Kevin Leyton-Brown
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6683)

Abstract

State-of-the-art algorithms for hard computational problems often expose many parameters that can be modified to improve empirical performance. However, manually exploring the resulting combinatorial space of parameter settings is tedious and tends to lead to unsatisfactory outcomes. Recently, automated approaches for solving this algorithm configuration problem have led to substantial improvements in the state of the art for solving various problems. One promising approach constructs explicit regression models to describe the dependence of target algorithm performance on parameter settings; however, this approach has so far been limited to the optimization of few numerical algorithm parameters on single instances. In this paper, we extend this paradigm for the first time to general algorithm configuration problems, allowing many categorical parameters and optimization for sets of instances. We experimentally validate our new algorithm configuration procedure by optimizing a local search and a tree search solver for the propositional satisfiability problem (SAT), as well as the commercial mixed integer programming (MIP) solver CPLEX. In these experiments, our procedure yielded state-of-the-art performance, and in many cases outperformed the previous best configuration approach.

Keywords

Local Search Random Forest General Algorithm Mixed Integer Programming Numerical Parameter 
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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References

  1. 1.
    Hutter, F., Hoos, H.H., Leyton-Brown, K.: Automated configuration of mixed integer programming solvers. In: Lodi, A., Milano, M., Toth, P. (eds.) CPAIOR 2010. LNCS, vol. 6140, pp. 186–202. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Minton, S., Johnston, M.D., Philips, A.B., Laird, P.: Minimizing conflicts: A heuristic repair method for constraint-satisfaction and scheduling problems. AIJ 58(1), 161–205 (1992)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Gratch, J., Dejong, G.: Composer: A probabilistic solution to the utility problem in speed-up learning. In: Proc. of AAAI 1992, pp. 235–240 (1992)Google Scholar
  4. 4.
    Adenso-Diaz, B., Laguna, M.: Fine-tuning of algorithms using fractional experimental design and local search. Operations Research 54(1), 99–114 (2006)CrossRefzbMATHGoogle Scholar
  5. 5.
    Birattari, M., Yuan, Z., Balaprakash, P., Stützle, T.: F-race and iterated F-race: an overview. In: Empirical Methods for the Analysis of Optimization Algorithms. Springer, Berlin (2010)Google Scholar
  6. 6.
    Birattari, M., Stützle, T., Paquete, L., Varrentrapp, K.: A racing algorithm for configuring metaheuristics. In: Proc. of GECCO 2002, pp. 11–18 (2002)Google Scholar
  7. 7.
    Hutter, F., Hoos, H.H., Stützle, T.: Automatic algorithm configuration based on local search. In: Proc. of AAAI 2007, pp. 1152–1157 (2007)Google Scholar
  8. 8.
    Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. JAIR 36, 267–306 (2009)zbMATHGoogle Scholar
  9. 9.
    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
  10. 10.
    Hutter, F., Babić, D., Hoos, H.H., Hu, A.J.: Boosting Verification by Automatic Tuning of Decision Procedures. In: Proc. of FMCAD 2007, pp. 27–34 (2007)Google Scholar
  11. 11.
    KhudaBukhsh, A., Xu, L., Hoos, H.H., Leyton-Brown, K.: SATenstein: Automatically building local search SAT solvers from components. In: Proc. of IJCAI 2009 (2009)Google Scholar
  12. 12.
    Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black box functions. Journal of Global Optimization 13, 455–492 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Bartz-Beielstein, T., Lasarczyk, C., Preuss, M.: Sequential parameter optimization. In: Proc. of CEC 2005, pp. 773–780. IEEE Press, Los Alamitos (2005)Google Scholar
  14. 14.
    Hutter, F., Hoos, H.H., Leyton-Brown, K., Murphy, K.P.: An experimental investigation of model-based parameter optimisation: SPO and beyond. In: Proc. of GECCO 2009 (2009)Google Scholar
  15. 15.
    Hutter, F., Hoos, H.H., Leyton-Brown, K., Murphy, K.P.: Time-bounded sequential parameter optimization. In: Blum, C., Battiti, R. (eds.) LION 4. LNCS, vol. 6073, pp. 281–298. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration (extended version). Technical Report TR-2010-10, UBC Computer Science (2010), http://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf
  18. 18.
    Bartz-Beielstein, T., Markon, S.: Tuning search algorithms for real-world applications: A regression tree based approach. In: Proc. of CEC 2004, pp. 1111–1118 (2004)Google Scholar
  19. 19.
    Baz, M., Hunsaker, B., Brooks, P., Gosavi, A.: Automated tuning of optimization software parameters. Technical Report TR2007-7, Univ. of Pittsburgh, Industrial Engineering (2007)Google Scholar
  20. 20.
    Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: SATzilla: portfolio-based algorithm selection for SAT. JAIR 32, 565–606 (2008)zbMATHGoogle Scholar
  21. 21.
    Leyton-Brown, K., Nudelman, E., Shoham, Y.: Empirical hardness models: Methodology and a case study on combinatorial auctions. Journal of the ACM 56(4), 1–52 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning, 2nd edn. Springer Series in Statistics. Springer, Heidelberg (2009)CrossRefzbMATHGoogle Scholar
  23. 23.
    Nell, C., Fawcett, C., Hoos, H.H., Leyton-Brown, K.: HAL: A framework for the automated analysis and design of high-performance algorithms. In: LION-5 (to appear, 2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Frank Hutter
    • 1
  • Holger H. Hoos
    • 1
  • Kevin Leyton-Brown
    • 1
  1. 1.University of British ColumbiaVancouverCanada

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