Almost all heuristic optimization procedures require the presence of a well-tuned set of parameters. The tuning of these parameters is usually a critical issue and may entail intensive computational requirements. We propose a fast and effective approach composed of two distinct stages. In the first stage, a genetic algorithm is applied to a small subset of representative problems to determine a few robust parameter sets. In the second stage, these sets of parameters are the starting points for a fast local search procedure, able to more deeply investigate the space of parameter sets for each problem to be solved. This method is tested on a parametric version of the Clarke and Wright algorithm and the results are compared with an enumerative parameter-setting approach previously proposed in the literature. The results of our computational testing show that our new parameter-setting procedure produces results of the same quality as the enumerative approach, but requires much shorter computational time.
Battarra, M., Golden, B., & Vigo, D. (2008). Tuning a Parametric Clarke-Wright Heuristic for the Vehicle Routing Through a Genetic Algorithm. Journal of the Operational Research Society, 59(11), 1568-1572. https://doi.org/10.1057/palgrave.jors.2602488