Boosting evolutionary algorithm configuration


Algorithm configuration has emerged as an essential technology for the improvement of high-performance solvers. We present new algorithmic ideas to improve state-of-the-art solver configurators automatically by tuning. Particularly, we introduce 1. a forward-simulation method to improve parallel performance, 2. an improvement to the configuration process itself, and 3. a new technique for instance-specific solver configuration. Extensive experimental results show that the new solver configurator compares very favorably with the state-of-the-art in automatic configuration for combinatorial solvers.

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  1. 1.

    Ahmadizadeh, K., Dilkina, B., Gomes, C.P., Sabharwal, A.: An empirical study of optimization for maximizing diffusion in networks. In: Proceedings of the 16th International Conference on Principles and Practice of Constraint Programming, pp. 514–521 (2010)

  2. 2.

    Ansótegui, C., Gabàs, J., Malitsky, Y., Sellmann, M.: Maxsat by improved instance-specific algorithm configuration. Artif. Intell. 235, 26–39 (2016)

    MathSciNet  Article  Google Scholar 

  3. 3.

    Ansótegui, C., Malitsky, Y., Samulowitz, H., Sellmann, M., Tierney, K.: Model-based genetic algorithms for algorithm configuration. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, pp. 733–739 (2015)

  4. 4.

    Ansotegui, C., Sellmann, M., Tierney, K.: A gender-based genetic algorithm for the automatic configuration of algorithms. In: Proceedings of the 15th International Conference on Principles and Practice of Constraint Programming, pp. 142–157 (2009)

  5. 5.

    Birattari, M., Yuan, Z., Balaprakash, P., Stützle, T.: F-race and iterated f-race: An overview Empirical Methods for the Analysis of Optimization Algorithms, pp. 311–336 (2010)

  6. 6.

    Şen, A., Atamtürk, A., Kaminsky, P.: A conic integer programming approach to constrained assortment optimization under the mixed multinomial logit model. Research Report BCOL.15.06, IEOR University of California–Berkeley (2015)

  7. 7.

    Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems., vol. 28, pp 2962–2970. Curran Associates, Inc. (2015)

  8. 8.

    Hamerly, G., Elkan, C.: Learning the k in k-means. In: In Neural Information Processing Systems, p. 2003. MIT Press (2003)

  9. 9.

    Hoos, H.H., Lindauer, M., Schaub, T.: claspfolio 2: Advances in algorithm selection for answer set programming. Theory Pract. Logic Program. 14(4–5), 569–585 (2014).

    Article  MATH  Google Scholar 

  10. 10.

    Hutter, F., Hamadi, Y.: Parameter adjustment based on performance prediction: Towards an instance-aware problem solver. Tech. Rep. MSR-TR-2005-125, Microsoft Research, Cambridge UK (2005)

  11. 11.

    Hutter, F., Hoos, H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Proceedings of the 5th International Conference on Learning and Intelligent Optimization, pp. 507–523 (2011)

  12. 12.

    Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: Paramils: An automatic algorithm configuration framework. J. Artif. Intell. Res. 36, 267–306 (2009).

    Article  MATH  Google Scholar 

  13. 13.

    Hutter, F., Lindauer, M., Balint, A., Bayless, S., Hoos, H., Leyton-Brown, K.: The configurable sat solver challenge (cssc). Artif. Intell. 243, 1–25 (2017).

    MathSciNet  Article  MATH  Google Scholar 

  14. 14.

    Hutter, F., López-Ibáñez, M., Fawcett, C., Lindauer, M., Hoos, H.H., Leyton-Brown, K., Stützle, T.: Aclib: A benchmark library for algorithm configuration. In: Pardalos, P. M., Resende, M. G., Vogiatzis, C., Walteros, J. L. (eds.) Learning and Intelligent Optimization, pp 36–40. Springer International Publishing, Cham (2014)

  15. 15.

    Kadioglu, S., Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Algorithm selection and scheduling. CP, 454–469 (2011)

  16. 16.

    Kadioglu, S., Malitsky, Y., Sellmann, M., Tierney, K.: ISAC–Instance-Specific Algorithm Configuration. In: Coelho, H., Studer, R., Wooldridge, M. (eds.) Proceedings of the 19th European Conference on Artificial Intelligence (ECAI), Frontiers in Artificial Intelligence and Applications, vol. 215, pp. 751–756. IOS Press (2010)

  17. 17.

    Leyton-Brown, K., Nudelman, E., Andrew, G., McFadden, J., Shoham, Y.: A portfolio approach to algorithm selection. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 1542–1543 (2003)

  18. 18.

    Lindauer, M., Feurer, M., Eggensperger, K., Klein, A., Falkner, S., Hutter, F.: Parallel SMAC (pSMAC) (2018).

  19. 19.

    Lindauer, M., Hutter, F.: Warmstarting of model-based algorithm configuration. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, pp. 1355–1362. AAAI Press. (2018)

  20. 20.

    López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Stützle, T., Birattari, M.: The irace package: Iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43–58 (2016).,

    MathSciNet  Google Scholar 

  21. 21.

    Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Algorithm portfolios based on cost-sensitive hierarchical clustering. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, pp. 608–614 (2013)

  22. 22.

    MaxSAT-Evaluations. (2019)

  23. 23.

    Papageorgiou, D.J., Nemhauser, G.L., Sokol, J., Cheon, M.S., Keha, A.B.: Mirplib – a library of maritime inventory routing problem instances: Survey, core model, and benchmark results. EJOR 235(2), 350–366 (2014). Maritime Logistics

    MathSciNet  Article  Google Scholar 

  24. 24.

    SAT-Competition: (2019).

  25. 25.

    Sheldon, D., Dilkina, B., Elmachtoub, A., Finseth, R., Sabharwal, A., Conrad, J., Gomes, C.P., Shmoys, D., Allen, W., Amundsen, O., Vaughan, B.: Maximizing spread of cascades using network design. In: UAI-2010: 26th Conference on Uncertainty in Artificial Intelligence, pp 517–526, Catalina Island (2010)

  26. 26.

    Xu, L., Hoos, H.H., Leyton-Brown, K.: Hydra: Automatically configuring algorithms for portfolio-based selection. AAAI (2010)

  27. 27.

    Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Satzilla2009: An automatic algorithm portfolio for sat. solver description (2009). SAT Competition

  28. 28.

    Xu, L., Hutter, F., Shen, J., Hoos, H.H., Leyton-Brown, K.: Satzilla2012: Improved algorithm selection based on cost-sensitive classification models (2012). SAT Competition

  29. 29.

    Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J., Ghodsi, A., Gonzalez, J., Shenker, S., Stoica, I.: Apache spark: A unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016).

    Article  Google Scholar 

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This work was partially supported by the MINECO-FEDER project TASSAT3 (TIN2016-76573-C2-2-P) and the MICINN project PROOFS (PID2019-109137GBC21).

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Correspondence to Josep Pon.

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Ansótegui, C., Pon, J. & Sellmann, M. Boosting evolutionary algorithm configuration. Ann Math Artif Intell (2021).

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  • Genetic algorithms
  • Algorithm configuration
  • Algorithm portfolios

Mathematics Subject Classification (2010)

  • 68