Abstract
In this paper, we apply a competitive coevolutionary approach using loosely coupled genetic algorithms to a distributed optimization of the Rosenbrock's function. The computational scheme is a coevolutionary system of agents with only local interaction among them, without any central synchronization. We use a recently developed coordination language called Manifold to implement our distributed optimization algorithm. We show that the distributed optimization algorithm implemented using Manifold outperforms the sequential optimization algorithm based on a standard genetic algorithm.KeywordsGenetic AlgorithmNash Equilibrium PointStandard Genetic AlgorithmGenetic Algorithm OperatorCoordination LanguageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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