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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
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)
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)
Adenso-Diaz, B., Laguna, M.: Fine-tuning of algorithms using fractional experimental design and local search. Operations Research 54(1), 99–114 (2006)
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)
Birattari, M., Stützle, T., Paquete, L., Varrentrapp, K.: A racing algorithm for configuring metaheuristics. In: Proc. of GECCO 2002, pp. 11–18 (2002)
Hutter, F., Hoos, H.H., Stützle, T.: Automatic algorithm configuration based on local search. In: Proc. of AAAI 2007, pp. 1152–1157 (2007)
Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. JAIR 36, 267–306 (2009)
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)
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)
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)
Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black box functions. Journal of Global Optimization 13, 455–492 (1998)
Bartz-Beielstein, T., Lasarczyk, C., Preuss, M.: Sequential parameter optimization. In: Proc. of CEC 2005, pp. 773–780. IEEE Press, Los Alamitos (2005)
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)
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)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
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
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)
Baz, M., Hunsaker, B., Brooks, P., Gosavi, A.: Automated tuning of optimization software parameters. Technical Report TR2007-7, Univ. of Pittsburgh, Industrial Engineering (2007)
Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: SATzilla: portfolio-based algorithm selection for SAT. JAIR 32, 565–606 (2008)
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)
Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning, 2nd edn. Springer Series in Statistics. Springer, Heidelberg (2009)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hutter, F., Hoos, H.H., Leyton-Brown, K. (2011). Sequential Model-Based Optimization for General Algorithm Configuration. In: Coello, C.A.C. (eds) Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, vol 6683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25566-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-25566-3_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25565-6
Online ISBN: 978-3-642-25566-3
eBook Packages: Computer ScienceComputer Science (R0)