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Decision making in an Adaptive Reservoir Genetic Algorithm

It is now common knowledge that blind search algorithms cannot perform with equal efficiency on all possible optimization problems defined on a domain. This knowledge applies also to Genetic Algorithms when viewed as global and blind optimizers. From this point of view it is necessary to design algorithms capable of adapting their search behaviour by making use in a direct fashion of the knowledge pertaining to the search landscape. The paper introduces a novel adaptive Genetic Algorithm where the exploration/exploitation balance is directly controlled using a Bayesian decision process. Test cases are analyzed as to how parameters affect the search behaviour of the algorithm.