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.