I. Introduction and state of the art
The fact that parallel evolutionary algorithms can obtain better results than sequential ones for the same computational effort [1] has been sometimes attributed to the fact that evolution proceeds differently in each node, and the effect that the immigrants from one node to another have on its diversity. The mating restriction that is inherent to the isolation of the population in several islands avoids premature convergence of the whole population, while the increased diversity attained with the incoming member of the other populations takes it closer to finding a solution. However, according to the intermediate disturbance hypothesis [2], the closer the immigrant is the current state of the population, the smaller effect it will have on the overall performance.