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Automatic Tuning of GRASP with Path-Relinking Heuristics with a Biased Random-Key Genetic Algorithm

  • Paola Festa
  • José F. Gonçalves
  • Mauricio G. C. Resende
  • Ricardo M. A. Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6049)

Abstract

GRASP with path-relinking (GRASP+PR) is a metaheuristic for finding optimal or near-optimal solutions of combinatorial optimization problems. This paper proposes a new automatic parameter tuning procedure for GRASP+PR heuristics based on a biased random-key genetic algorithm (BRKGA). Given a GRASP+PR heuristic with n input parameters, the tuning procedure makes use of a BRKGA in a first phase to explore the parameter space and set the parameters with which the GRASP+PR heuristic will run in a second phase. The procedure is illustrated with a GRASP+PR for the generalized quadratic assignment problem with n = 30 parameters. Computational results show that the resulting hybrid heuristic is robust.

Keywords

Hybrid Heuristic Automatic Tune Tuning Procedure Elite Solution Elite Individual 
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 2010

Authors and Affiliations

  • Paola Festa
    • 1
  • José F. Gonçalves
    • 2
  • Mauricio G. C. Resende
    • 3
  • Ricardo M. A. Silva
    • 4
  1. 1.University of Napoli “Federico II”NapoliItaly
  2. 2.Universidade do PortoPortoPortugal
  3. 3.AT&T Labs ResearchFlorham ParkUSA
  4. 4.Universidade Federal de LavrasLavrasBrazil

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