Automatic Tuning of GRASP with Evolutionary Path-Relinking

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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