Abstract
Modern solvers for hard computational problems often expose parameters that permit customization for high performance on specific instance types. Since it is tedious and time-consuming to manually optimize such highly parameterized algorithms, recent work in the AI literature has developed automated approaches for this algorithm configuration problem [1, 3, 10, 11, 13, 16].
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References
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)
Balint, A., Fröhlich, A., Tompkins, D., Hoos, H.: Sparrow 2011. In: Booklet of SAT-2011 Competition (2011)
Birattari, M., Stützle, T., Paquete, L., Varrentrapp, K.: A racing algorithm for configuring metaheuristics. In: Proceedings of GECCO-02, pp. 11–18 (2002)
Chiarandini, M., Fawcett, C., Hoos, H.: A modular multiphase heuristic solver for post enrolment course timetabling. In: Proceedings of PATAT-08 (2008)
Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T.: A hybrid TP\(+\)PLS algorithm for bi-objective flow-shop scheduling problems. Comput. Oper. Res. 38(8), 1219–1236 (2011)
Fawcett, C., Helmert, M., Hoos, H.H., Karpas, E., Röger, G., Seipp, J.: FD-autotune: domain-specific configuration using fast-downward. In: Proceedings of ICAPS-PAL11 (2011)
Hutter, F., Babić, D., Hoos, H.H., Hu, A.J.: Boosting verification by automatic tuning of decision procedures. In: Proceedings of FMCAD-07, pp. 27–34 (2007)
Hutter, F., Balint, A., Bayless, S., Hoos, H., Leyton-Brown, K.: Configurable SAT solver challenge (CSSC) (2013), riptsizehttp://www.cs.ubc.ca/labs/beta/Projects/CSSC2013/
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)
Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507–523. Springer, Heidelberg (2011)
Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. JAIR 36, 267–306 (2009)
KhudaBukhsh, A., Xu, L., Hoos, H.H., Leyton-Brown, K.: SATenstein: automatically building local search SAT solvers from components. In: Proceedings of IJCAI-09, pp. 517–524 (2009)
López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T., Birattari, M.: The irace package, iterated race for automatic algorithm configuration. Technical report TR/IRIDIA/2011-004, IRIDIA, Université Libre de Bruxelles, Belgium (2011)
López-Ibáñez, M., Stützle, T.: The automatic design of multi-objective ant colony optimization algorithms. IEEE Trans. Evol. Comput. 16(6), 861–875 (2012)
López-Ibáñez, M., Stützle, T.: Automatically improving the anytime behaviour of optimisation algorithms. Eur. J. Oper. Res. (2013)
Nannen, V., Eiben, A.: Relevance estimation and value calibration of evolutionary algorithm parameters. In: Proc. of IJCAI-07, pp. 975–980 (2007)
Roussel, O.: Controlling a solver execution with the runsolver tool. JSAT 7(4), 139–144 (2011)
Silverthorn, B., Lierler, Y., Schneider, M.: Surviving solver sensitivity: an ASP practitioner’s guide. In: Proceedings of ICLP-LIPICS-12, pp. 164–175 (2012)
Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of KDD-2013, pp. 847–855 (2013)
Tompkins, D.A.D., Balint, A., Hoos, H.H.: Captain Jack: new variable selection heuristics in local search for SAT. In: Sakallah, K.A., Simon, L. (eds.) SAT 2011. LNCS, vol. 6695, pp. 302–316. Springer, Heidelberg (2011)
Vallati, M., Fawcett, C., Gerevini, A.E., Hoos, H.H., Saetti, A.: Generating fast domain-optimized planners by automatically configuring a generic parameterised planner. In: Proceedings of ICAPS-PAL11 (2011)
Acknowledgments
We gratefully acknowledge all authors of algorithms and instance distributions for making their work available (they are cited on the webpage, acknowledged in README files, and will be cited in a future longer version of this paper). We thank Kevin Tierney and Yuri Malitsky for modifying GGA [1] to support AClib’s format; Lin Xu for generating several instance distributions and writing most feature extraction code for SAT and TSP; Adrian Balint and Sam Bayless for contributing SAT benchmark distributions; Mauro Vallati for exposing many new parameters in LPG; the developers of Fast Downward for helping define its configuration space; and Steve Ramage for helping diagnose and fix problems with several wrappers and runsolver. M. Lindauer acknowledges support by DFG project SCHA 550/8-3, and M. López-Ibáñez acknowledges support from a “Crédit Bref Séjour à l’étranger” from the Belgian F.R.S.-FNRS.
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Hutter, F. et al. (2014). AClib: A Benchmark Library for Algorithm Configuration. In: Pardalos, P., Resende, M., Vogiatzis, C., Walteros, J. (eds) Learning and Intelligent Optimization. LION 2014. Lecture Notes in Computer Science(), vol 8426. Springer, Cham. https://doi.org/10.1007/978-3-319-09584-4_4
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