Proposal for Improvement of GRASP Metaheuristic and Genetic Algorithm Using the Q-Learning Algorithm

Currently many non-tractable considered problems have been solved satisfactorily through methods of approximate optimization called metaheuristic. These methods use non- deterministic approaches that find good solutions which, however, do not guarantee the determination of the global optimum. The success of a metaheuristic is conditioned its capacity to adequately alternate between exploration and exploitation of the solutions space. A way to guide such algorithms during the searching for better solutions is supplying them with more knowledge of the environment. This work proposes the use of a technique of Reinforcement Learning - Q-Learning Algorithm - for the constructive phase of GRASP metaheuristic and also as generator of the initial population for the Genetic Algorithm. The proposed methods will be applied to the symmetrical traveling salesman problem.