Theory of Coevolutionary Genetic Algorithms

We discuss stochastic modeling of scaled coevolutionary genetic algorithms (coevGA) which converge asymptotically to global optima. In our setting, populations contain several types of interacting creatures such that for some types (appropriately defined) globally maximal creatures exist. These algorithms particularly demand parallel processing in view of the nature of the fitness function. It is shown that coevolutionary arms races yielding global optima can be implemented in a procedure similar to simulated annealing.

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