Advertisement

MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework

  • Aymeric Blot
  • Holger H. Hoos
  • Laetitia Jourdan
  • Marie-Éléonore Kessaci-Marmion
  • Heike Trautmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10079)

Abstract

Automated algorithm configuration procedures play an increasingly important role in the development and application of algorithms for a wide range of computationally challenging problems. Until very recently, these configuration procedures were limited to optimising a single performance objective, such as the running time or solution quality achieved by the algorithm being configured. However, in many applications there is more than one performance objective of interest. This gives rise to the multi-objective automatic algorithm configuration problem, which involves finding a Pareto set of configurations of a given target algorithm that characterises trade-offs between multiple performance objectives. In this work, we introduce MO-ParamILS, a multi-objective extension of the state-of-the-art single-objective algorithm configuration framework ParamILS, and demonstrate that it produces good results on several challenging bi-objective algorithm configuration scenarios compared to a base-line obtained from using a state-of-the-art single-objective algorithm configurator.

Keywords

Algorithm configuration Parameter tuning Multi-objective optimisation Local search algorithms 

References

  1. 1.
    Adenso-Díaz, B., Laguna, M.: Fine-tuning of algorithms using fractional experimental designs and local search. Oper. Res. 54(1), 99–114 (2006)CrossRefzbMATHGoogle Scholar
  2. 2.
    Birattari, M., Yuan, Z., Balaprakash, P., Stützle, T.: F-Race and iterated F-Race: an overview. In: Bartz-Beielstein, T., Chiarandini, M., Paquete, L., Preuss, M. (eds.) Experimental Methods for the Analysis of Optimization Algorithms, pp. 311–336. Springer, Berlin (2010)CrossRefGoogle Scholar
  3. 3.
    Blot, A., Aguirre, H., Dhaenens, C., Jourdan, L., Marmion, M.-E., Tanaka, K.: Neutral but a winner! How neutrality helps multiobjective local search algorithms. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C.C. (eds.) EMO 2015. LNCS, vol. 9018, pp. 34–47. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-15934-8_3 Google Scholar
  4. 4.
    Geiger, M.J.: Foundations of the Pareto iterated local search metaheuristic. CoRR abs/0809.0406 (2008). http://arxiv.org/abs/0809.0406
  5. 5.
    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). doi: 10.1007/978-3-642-25566-3_40 CrossRefGoogle Scholar
  6. 6.
    Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. JAIR 36, 267–306 (2009)zbMATHGoogle Scholar
  7. 7.
    Hutter, F., Hoos, H.H., Stützle, T.: Automatic algorithm configuration based on local search. In: AAAI 2007, pp. 1152–1157 (2007)Google Scholar
  8. 8.
    Knowles, J., Thiele, L., Zitzler, E.: A tutorial on the performance assessment of stochastic multiobjective optimizers. Technical report 214, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Switzerland, revised version (2006)Google Scholar
  9. 9.
    Liefooghe, A., Humeau, J., Mesmoudi, S., Jourdan, L., Talbi, E.: On dominance-based multiobjective local search: design, implementation and experimental analysis on scheduling and traveling salesman problems. J. Heuristics 18(2), 317–352 (2012)CrossRefGoogle Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    Lourenço, H., Martin, O., Stützle, T.: Iterated local search: framework and applications. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics, vol. 2, pp. 363–397. Springer, New York (2010)CrossRefGoogle Scholar
  12. 12.
    Marmion, M.-E., Mascia, F., López-Ibáñez, M., Stützle, T.: Automatic design of hybrid stochastic local search algorithms. In: Blesa, M.J., Blum, C., Festa, P., Roli, A., Sampels, M. (eds.) HM 2013. LNCS, vol. 7919, pp. 144–158. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38516-2_12 CrossRefGoogle Scholar
  13. 13.
    Zhang, T., Georgiopoulos, M., Anagnostopoulos, G.C.: S-Race: a multi-objective racing algorithm. In: GECCO 2013, pp. 1565–1572 (2013)Google Scholar
  14. 14.
    Zhang, T., Georgiopoulos, M., Anagnostopoulos, G.C.: SPRINT multi-objective model racing. In: GECCO 2015, pp. 1383–1390 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Aymeric Blot
    • 1
    • 2
    • 3
  • Holger H. Hoos
    • 3
  • Laetitia Jourdan
    • 1
  • Marie-Éléonore Kessaci-Marmion
    • 1
  • Heike Trautmann
    • 4
  1. 1.Université de Lille, Inria, CNRS, UMR 9189 – CRIStALLilleFrance
  2. 2.École Normale Supérieure de RennesRennesFrance
  3. 3.University of British ColumbiaVancouverCanada
  4. 4.University of MünsterMünsterGermany

Personalised recommendations