Applications of Racing Algorithms: An Industrial Perspective

  • Sven Becker
  • Jens Gottlieb
  • Thomas Stützle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3871)


Stochastic local search (SLS) methods like evolutionary algorithms, ant colony optimisation or iterated local search receive an ever increasing attention for the solution of highly application relevant optimisation problems. Despite their noteworthy successes, several issues still hinder their even wider spread. One central issue is the configuration and parameterisation of SLS methods, which is known to be a time- and personal-intensive process. Recently, several attempts have been made to automate the tuning of SLS algorithms. One of the most promising directions is the usage of the racing methodology, which is a statistical method for selecting promising candidate configurations. We present results of a study on the application of this methodology to the tuning of a complex SLS method for an industrial vehicle scheduling and routing problem, and compare the performance of two racing methods.


Local Search Memetic Algorithm Iterate Local Search Stochastic Local Search Local Search Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hoos, H.H., Stützle, T.: Stochastic Local Search—Foundations and Applications. Morgan Kaufmann, San Francisco (2004)MATHGoogle Scholar
  2. 2.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)MATHGoogle Scholar
  3. 3.
    Moscato, P., Cotta, C.: A gentle introduction to memetic algorithms. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 105–144. Kluwer Academic Publishers, Norwell (2002)Google Scholar
  4. 4.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)MATHGoogle Scholar
  5. 5.
    Lourenço, H.R., Martin, O., Stützle, T.: Iterated local search. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 321–353. Kluwer Academic Publishers, Norwell (2002)Google Scholar
  6. 6.
    Xu, J., Chiu, S.Y., Glover, F.: Fine-tuning a tabu search algorithm with statistical tests. International Transactions in Operational Research 5(4), 233–244 (1998)CrossRefGoogle Scholar
  7. 7.
    Coy, S.P., Golden, B.L., Runger, G.C., Wasil, E.A.: Using experimental design to find effective parameter settings for heuristics. Journal of Heuristics 7(1), 77–97 (2001)CrossRefMATHGoogle Scholar
  8. 8.
    Adenso-Díaz, B., Laguna, M.: Fine-tuning of algorithms using fractional experimental designs and local search. Operations Research (in press)Google Scholar
  9. 9.
    Birattari, M., Stützle, T., Paquete, L., Varrentrapp, K.: A racing algorithm for configuring metaheuristics. In: Langdon, W.B., others (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), pp. 11–18. Morgan Kaufmann, San Francisco (2002)Google Scholar
  10. 10.
    Birattari, M.: The Problem of Tuning Metaheuristics. PhD thesis, IRIDIA, Université Libre de Bruxelles, Belgium (2004)Google Scholar
  11. 11.
    Siegel, S., Jr., N.J.C., Castellan, N.J.: Nonparametric Statistics for the Behavioral Sciences, 2nd edn. McGraw Hill, New York (2000)MATHGoogle Scholar
  12. 12.
    Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. Chapman & Hall / CRC (2000)Google Scholar
  13. 13.
    Chiarandini, M., Birattari, M., Socha, K., Rossi-Doria, O.: An effective hybrid approach for the university course timetabling problem. Journal of Scheduling (submitted)Google Scholar
  14. 14.
    Maron, O., Moore, A.W.: Hoeffding races: Accelerating model selection search for classification and function approximation. In: Cowan, J.D., Tesauro, G., Alspector, J. (eds.) Advances in Neural Information Processing Systems, vol. 6, pp. 59–66. Morgan Kaufmann Publishers, Inc, San Francisco (1994)Google Scholar
  15. 15.
    Moore, A.W., Lee, M.S.: Efficient algorithms for minimizing cross validation error. In: International Conference on Machine Learning, pp. 190–198. Morgan Kaufmann Publishers, Inc., San Francisco (1994)Google Scholar
  16. 16.
    Conover, W.J.: Practical Nonparametric Statistics, 3rd edn. John Wiley & Sons, New York (1999)Google Scholar
  17. 17.
    Toth, P., Vigo, D. (eds.): The Vehicle Routing Problem. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sven Becker
    • 1
  • Jens Gottlieb
    • 2
  • Thomas Stützle
    • 3
  1. 1.VEGA Informations-Technologien GmbHDarmstadtGermany
  2. 2.SAP AGWalldorfGermany
  3. 3.Computer Science DepartmentDarmstadt University of TechnologyDarmstadtGermany

Personalised recommendations