Off-line and On-line Tuning: A Study on Operator Selection for a Memetic Algorithm Applied to the QAP

  • Gianpiero Francesca
  • Paola Pellegrini
  • Thomas Stützle
  • Mauro Birattari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6622)


Tuning methods for selecting appropriate parameter configurations of optimization algorithms have been the object of several recent studies. The selection of the appropriate configuration may strongly impact on the performance of evolutionary algorithms. In this paper, we study the performance of three memetic algorithms for the quadratic assignment problem when their parameters are tuned either off-line or on-line. Off-line tuning selects a priori one configuration to be used throughout the whole run for all the instances to be tackled. On-line tuning selects the configuration during the solution process, adapting parameter settings on an instance-per-instance basis, and possibly to each phase of the search. The results suggest that off-line tuning achieves a better performance than on-line tuning.


Local Search Mutation Operator Crossover Operator Memetic Algorithm Quadratic Assignment Problem 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gianpiero Francesca
    • 1
  • Paola Pellegrini
    • 2
  • Thomas Stützle
    • 2
  • Mauro Birattari
    • 2
  1. 1.Dipartimento di IngegneriaUniversity of SannioBeneventoItaly
  2. 2.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium

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