Fine-Tuning Algorithm Parameters Using the Design of Experiments Approach

  • Aldy Gunawan
  • Hoong Chuin Lau
  • Lindawati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6683)


Optimizing parameter settings is an important task in algorithm design. Several automated parameter tuning procedures/configurators have been proposed in the literature, most of which work effectively when given a good initial range for the parameter values. In the Design of Experiments (DOE), a good initial range is known to lead to an optimum parameter setting. In this paper, we present a framework based on DOE to find a good initial range of parameter values for automated tuning. We use a factorial experiment design to first screen and rank all the parameters thereby allowing us to then focus on the parameter search space of the important parameters. A model based on the Response Surface methodology is then proposed to define the promising initial range for the important parameter values. We show how our approach can be embedded with existing automated parameter tuning configurators, namely ParamILS and RCS (Randomized Convex Search), to tune target algorithms and demonstrate that our proposed methodology leads to improvements in terms of the quality of the solutions.


parameter tuning algorithm design of experiments response surface methodology 


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  1. 1.
    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
  2. 2.
    Barr, R.S., Golden, B.L., Kelly, J.P., Resende, M.G.C., Stewart, W.R.: Designing and Reporting on Computational Experiments with Heuristic Methods. Journal of Heuristics 1, 9–32 (1995)CrossRefzbMATHGoogle Scholar
  3. 3.
    Birattari, M., Stützle, T., Paquete, L., Varrentrapp, K.: A Racing Algorithm for Configuring Metaheuristics. In: Proc. Of the Genetic and Evolutionary Computation Conference, pp. 11–18. Morgan Kaufmann, San Francisco (2002)Google Scholar
  4. 4.
    Box, G., Wilson, K.: On the Experimental Attainment of Optimum Conditions. Journal of the Royal Statistical Society Series b 13, 1–45 (1951)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Burkard, R.E., Karisch, S.E., Rendl, F.: QAPLIB – A Quadratic Assignment Problem Library. Journal of Global Optimization 10, 391–403 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Caserta, M., Voß, S.: A Math-Heuristic Algorithm for the DNA Sequencing Problem. In: Blum, C., Battiti, R. (eds.) LION 4. LNCS, vol. 6073, pp. 25–36. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Caserta, M., Voß, S.: Corridor Selection and Fine Tuning for the Corridor Method. In: Stützle, T. (ed.) LION 3. LNCS, vol. 5851, pp. 163–175. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Halim, S., Yap, R., Lau, H.C.: An Integrated White+Black Box Approach for Designing and Tuning Stochastic Local Search. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 332–347. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Hutter, F., Hoos, H.H., Leyton-Brown, K., Murphy, K.: Time-Bounded Sequential Parameter Optimization. In: Blum, C., Battiti, R. (eds.) LION 4. LNCS, vol. 6073, pp. 281–298. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: An Automatic Algorithm Configuration Framework. Journal of Artificial Intelligence Research 36, 267–306 (2009)zbMATHGoogle Scholar
  11. 11.
    Lau, H.C., Xiao, F.: A Framework for Automated Parameter Tuning in Heuristic Design. In: 8th Metaheuristics International Conference, Hamburg, Germany (2009)Google Scholar
  12. 12.
    Lourenco, H.R., Martin, O.C., Stutzle, T.: Iterated Local Search. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Sci., vol. 57, pp. 320–353. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Montgomery, D.C.: Design and analysis of Experiments, 6th edn. John Wiley and Sons Inc., Chichester (2005)zbMATHGoogle Scholar
  14. 14.
    Ng, K.M., Gunawan, A., Poh, K.L.: A hybrid Algorithm for the Quadratic Assignment Problem. In: Proc. International Conference on Conference on Scientific Computing, Nevada, USA, pp. 14–17 (2008)Google Scholar
  15. 15.
    Parsons, R., Johnson, M.: A Case Study in Experimental Design Applied to Genetic Algorithms with Application to DNA Sequence Assembly. Journal of Mathematical and Management Sciences 17(3), 369–396 (1997)Google Scholar
  16. 16.
    Ridge, E., Kudenko, D.: Tuning the Performance of the MMAS Heuristic. In: Stützle, T., Birattari, M., Hoos, H.H. (eds.) SLS 2007. LNCS, vol. 4638, pp. 46–60. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Taillard, E.D.: Comparison of Iterative Searches for the Quadratic Assignment Problem. Location Science 3(2), 87–105 (1995)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aldy Gunawan
    • 1
  • Hoong Chuin Lau
    • 1
  • Lindawati
    • 1
  1. 1.School of Information SystemsSingapore Management UniversitySingapore

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