Recent research has shown that the hybridization of metaheuristics is a powerful mechanism to develop more robust and efficient methods to solve hard optimization problems. The combination of different techniques and concepts behind metaheuristics, if well designed, has the potential to exploit their advantages while diminishing their drawbacks, which results in methods suited to a more diverse set of real problems. The DM‐GRASP heuristic is one such hybrid method that has achieved promising results. It is a hybrid version of the GRASP metaheuristic that incorporates a data‐mining process. In this work, we review how this hybridization was designed and survey the results of its practical applications evaluated until now.