Devising adaptive migration policies for cooperative distributed genetic algorithms

Distributed genetic algorithms (DGAs) constitute an interesting approach to undertake the premature convergence problem in evolutionary optimization. This is done by spatial partitioning a huge panmitic population into several semi-isolated groups, called demes, each evolving in parallel by its own pace, and possibly exploring different regions of the search space. At the center of such approach lies the migratory process that simulates the swapping of individuals belonging to different demes, in such a way to ensure the sharing of good genetic material. In this paper, we model the migration step in DGAs as an explicit means to promote cooperation among genetic agents, autonomous entities encapsulating GA instances for possibly tackling different sub-problems of a complicated task. The focus is on the characterization of adaptive migration policies in which the choice of what individuals to migrate and/or replace is not defined a priori but according to a more knowledge-oriented rule. Comparative results obtained for a data-mining task were conducted, in order to assess the performance of adaptive migration according to efficiency/effectiveness criteria.