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Decentralized Cellular Evolutionary Algorithms

In this chapter we study cellular evolutionary algorithms, a kind of decentralized heuristics, and the importance of their induced exploration/exploitation balance on different problems. It is shown that, by choosing synchronous or asynchronous update policies, the selection pressure, and thus the exploration/exploitation tradeoff, can be influenced directly, without using additional ad hoc parameters. The same effect can be obtained by using synchronous algorithms of different neighborhood-to-topology ratio. All the discussed algorithms are applied to a set of benchmark problems. Our conclusions show that the update methods of the asynchronous versions, as well as the ratio of the decentralized algorithm, have a marked influence on the efficiency and accuracy of the resulting algorithm.