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. The Q-Credit Assignment (QCA) is a method, based on Qlearning,for allocating credit to rules in Classifier Systems with internalstate. It is more powerful than other proposed methods, because it correctlyevaluates shared rules, but it has a large computational cost, due tothe Multi-Layer Perceptron (MLP) that stores the evaluation function.We present a method for reducing this cost by reducing redundancy inthe input space of the MLP through feature extraction. The experimentalresults...

Q-learning and redundancy reduction in classifier systems with internal state