A Comparison of Operator Utility Measures for On-Line Operator Selection in Local Search

  • Nadarajen Veerapen
  • Jorge Maturana
  • Frédéric Saubion
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7219)


This paper investigates the adaptive selection of operators in the context of Local Search. The utility of each operator is computed from the solution quality and distance of the candidate solution from the search trajectory. A number of utility measures based on the Pareto dominance relationship and the relative distances between the operators are proposed and evaluated on QAP instances using an implied or static target balance between exploitation and exploration. A refined algorithm with an adaptive target balance is then examined.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Birattari, M.: The Problem of Tuning Metaheuristics as seen from a machine learning perspective. Ph.D. thesis, Université Libre de Bruxelles, Brussels, Belgium (December 2004)Google Scholar
  2. 2.
    Burkard, R.E., Karisch, S.E., Rendl, F.: QAPLIB – a quadratic assignment problem library. Journal of Global Optimization 10, 391–403 (1997)MATHMathSciNetCrossRefGoogle Scholar
  3. 3.
    Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: An emerging direction in modern search technology. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 57, pp. 457–474. Springer, New York (2003)Google Scholar
  4. 4.
    Fialho, Á.: Adaptive Operator Selection for Optimization. Ph.D. thesis, Université Paris-Sud 11, Orsay, France (December 2010)Google Scholar
  5. 5.
    Hamadi, Y., Monfroy, E., Saubion, F.: What is autonomous search? In: van Hentenryck, P., Milano, M. (eds.) Hybrid Optimization. Springer Optimization and Its Applications, vol. 45, pp. 357–391. Springer, New York (2011)CrossRefGoogle Scholar
  6. 6.
    Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.): Parameter Setting in Evolutionary Algorithms. SCI, vol. 54. Springer, Heidelberg (2007)MATHGoogle Scholar
  7. 7.
    Maturana, J., Lardeux, F., Saubion, F.: Autonomous operator management for evolutionary algorithms. J. Heuristics 16(6), 881–909 (2010)MATHCrossRefGoogle Scholar
  8. 8.
    Maturana, J., Saubion, F.: On the Design of Adaptive Control Strategies for Evolutionary Algorithms. In: Monmarché, N., Talbi, E.-G., Collet, P., Schoenauer, M., Lutton, E. (eds.) EA 2007. LNCS, vol. 4926, pp. 303–315. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Taillard, É.D.: Robust taboo search for the quadratic assignment problem. Parallel Computing 17(4-5), 443–455 (1991)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Veerapen, N., Saubion, F.: Pareto Autonomous Local Search. In: Coello, C.A.C. (ed.) LION 5. LNCS, vol. 6683, pp. 392–406. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nadarajen Veerapen
    • 1
  • Jorge Maturana
    • 2
  • Frédéric Saubion
    • 1
  1. 1.LERIALUNAM Université, Université d’AngersAngersFrance
  2. 2.Instituto de InformáticaUniversidad Austral de ChileValdiviaChile

Personalised recommendations