A parallel hybrid implementation using genetic algorithm, GRASP and reinforcement learning

In the process of searching for better solutions, a metaheuristic can be guided to regions of promising solutions using the acquisition of information on the problem under study. In this work this is done through the use of reinforcement learning. The performance of a metaheuristic can also be improved using multiple search trajectories, which act competitively and/or cooperatively. This can be accomplished using parallel processing. Thus, in this paper we propose a hybrid parallel implementation for the GRASP metaheuristics and the genetic algorithm, using reinforcement learning, applied to the symmetric traveling salesman problem.