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

P. Festa(paola.festa***at***unina.it)
J.F. Gonçalves(jfgoncal***at***fep.up.pt)
M.G.C. Resende(mgcr***at***research.att.com)
R.M.A. Silva(rmas***at***dcc.ufla.br)

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: Automatic tuning of parameters, GRASP, path-relinking, algorithm engineering, metaheuristics

Category 1: Combinatorial Optimization (Meta Heuristics )

Citation: AT&T Labs Research Technical Report, AT&T Labs Research, Florham Park, NJ 07932, Feb, 2010.

Download: [PDF]

Entry Submitted: 02/16/2010
Entry Accepted: 02/16/2010
Entry Last Modified: 02/16/2010

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