Theory of coevolutionary genetic algorithms

Authors:
Lothar M. Schmitt
Affiliations:
The University of Aizu, Aizu-Wakamatsu City, Fukushima Pref., Japan
Venue:
ISPA'03 Proceedings of the 2003 international conference on Parallel and distributed processing and applications
Year:
2003

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Abstract

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.