Extending the Hybridization of Metaheuristics with Data Mining to a Broader Domain

The incorporation of data mining techniques into metaheuristics has been efficiently adopted to solve several optimization problems. Nevertheless, we observe in the literature that this hybridization has been limited to problems in which the solutions are characterized by sets of (unordered) elements. In this work, we develop a hybrid data mining metaheuristic to solve a problem for which solutions are defined by sequences of elements. This way, we extend the domain of combinatorial optimization problems which can benefit from the combination of data mining and metaheuristic. Computational experiments showed that the proposed approach improves the pure algorithm both in the average quality of the solution and in execution time.