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Distributed population models improve the performance of genetic algorithms by assisting the selection scheme in maintaining diversity. A significant concern with these systems is that they need to be carefully configured in order to operate at their optimum. Failure to do so can often result in performance that is significantly under that of an equivalent panmitic implementation. We introduce a new distributed GA that requires little additional configuration over a panmitic GA. Early experimentation with this paradigm indicates that it is able to improve the searching abilities of the genetic algorithm on some problem domains.