Heterogeneity through Proactivity: Enhancing Distributed EAs

This work proposes an heterogeneous distributed evolutionary algorithm that automatically adapts its migration policy based on the entropy of the population. It is an heterogeneous algorithm since the search performed by each subpopulation is different from each other. the novelty of our approach lies on its proactivity, in which each subpopulation can ask for more/less frequent migrations from its neighbors in order to maintain the genetic diversity at a desired level. the goal is to avoid the subpopulations to get trapped into local minima. the results on large NK-landscape instances have shown that the proactive strategy is a very promising approach, specially for highly rugged landscapes, in which it does not only reaches the most accurate solutions, but it does the fastest.