Automatic tuning of GRASP with path-relinking heuristics with a biased random-key genetic algorithm

Authors:
Paola Festa;José F. Gonçalves;Mauricio G. C. Resende;Ricardo M. A. Silva
Affiliations:
University of Napoli “Federico II”, Napoli, Italy;Universidade do Porto, Porto, Portugal;ATST Labs Research, Florham Park, NJ;Universidade Federal de Lavras, Lavras, MG, Brazil
Venue:
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
Year:
2010

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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.