Metaheuristic methods have been demonstrated to be efficient tools to solve hard optimization problems. Most metaheuristics define a set of parameters that must be tuned. A good setup of that parameter valuescan lead to take advantage of the metaheuristic capabilities to solve the problem at hand. Tuning strategiesare step by step methods based on multiple runs of the metaheuristic algorithm. In this study we comparefour automated tuning methods: F-Race, Revac, ParamILS and SPO. We evaluate the performance of eachmethod using a standard genetic algorithm for continuous function optimization. We discuss about therequirements of each method, the resources used and quality of solutions found in different scenarios.Finally we establish some guidelines that can help to choose the more appropriate tuning procedure.
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