Automatic Tuning of GRASP with Path-Relinking in data clustering with F-R ace and iterated F-Race

In studies that use metaheuristics although the input parameters directly influence the performance of the algorithm its definition is mostly done manually raising questions about the quality of the results. This paper aims to apply the F/I-Race in the self parameterization of GRASP with Path-Relinking in the data clustering in order to obtain better results than the manually tuned algorithms. Experiments performed with five data sets showed that the use of I/F-race contributed to achievement best results than manual tuning

AUTOMATIC TUNING OF GRASP WITH EVOLUTIONARY PATH-RELINKING

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. 1