In this paper we compare different policies to select individuals to migrate in an island
model. Our thesis is that choosing individuals in a way that exploits differences between
populations can enhance diversity, and improve the system performance. This has lead us to
propose a family of policies that we call multikulti, in which nodes exchange individuals
different "enough" among them. In this paper we present a policy according to which the
receiver node chooses the most different individual among the sample received from the sending
node. This sample is randomly built but only using individuals with a fitness above a threshold.
This threshold is previously established by the receiving node. We have tested our system in two
problems previously used in the evaluation of parallel systems, presenting different degree of
difficulty. The multikulti policy presented herein has been proved to be more robust than other
usual migration policies, such as sending the best or a random individual.