L.F. Morán-Mirabal, J.L. González-Velarde, and M.G.C. Resende
To appear in Proceedings of Hybrid Metaheuristics (MH 2013), LNCS
ABSTRACT
Heuristics for
combinatorial optimization are often controlled by discrete and
continuous parameters that define its behavior. The number of possible
configurations of the heuristic can be large, resulting in a difficult
analysis. Manual tuning can be time-consuming, and usually considers a
very limited number of configurations. An alternative to manual tuning
is automatic tuning. In this paper, we present a scheme for automatic
tuning of GRASP with evolutionary path-relinking heuristics. The
proposed scheme uses a biased random-key genetic algorithm (BRKGA) to
determine good configurations. We illustrate the tuning procedure with
experiments on three optimization problems: set covering, maximum cut,
and node capacitated graph partitioning. For each problem we
automatically tune a specific GRASP with evolutionary path-relinking
heuristic to produce fast effective procedures.
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Last modified: 16 April 2013