Multikulti algorithm: Using genotypic differences in adaptive distributed evolutionary algorithm migration policies

Migration policies in distributed evolutionary algorithms are bound to have, as much as any other evolutionary operator, an impact on the overall performance. However, they have not been an active area of research until recently, and this research has concentrated on the migration rate. In this paper we compare different migration policies, including our proposed multikulti methods, which choose the individuals that are going to be sent to other nodes based on the principle of multiculturalism: the individual sent should be as different as possible to the receiving population (represented in several possible ways). We have checked this policy on two discrete optimization problems for different number of nodes, and found that, in average or in median, multikulti policies outperform others like sending the best or a random individual; however, their advantage changes with the number of nodes involved and the difficulty of the problem. The success of these kind of policies is explained via the measurement of entropies, which are known to have an impact in the performance of the evolutionary algorithm.