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

Submitted to 9th International Symposium on Experimental Algorithms (SEA 2010)

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+P  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.

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