“…Regarding the processing time during the prediction stage, the exploitation of the properties (6) and (15) enables a quick mapping through GA. Moreover, our MAP algorithm seems consistent, in the sense that it presents a small standard deviation on the CPU time, as can be seen in Table XVI, which summarizes the mean and standard deviation values of the CPU time demanded to solve the MAP problem for the chosen stories, running on quadcore processor, by using our special formulation of GA, henceforward called SGA, and two baseline algorithms: the usual GA (without exploiting the properties given by (6) and (15)) and random-restart hill climbing (RRHC). In this experiment the number of GA individuals and the number of restarting loops (in the case of RRHC algorithm) were chosen aiming at overcoming local minima, in such a way that the choice of the MAP algorithm has no impact on the performance indices.…”