The Anticipatory Classifier System (ACS) is able to form a complete internal representation of an environment. Unlike most other classifer system and reinforcement learning approaches, it is able to learn latently (i.e. to learn in an environment without getting any reward) and to form an internal model of the perceived environment. After the observation that the model is not necessarily maximally general a genetic generalization pressure was introduced to the ACS. This paper focuses on the different mechanisms in the anticipatory learning process, which resembles the specialization pressure, and in the genetic algorithm, which realizes the genetic generalization pressure. The capability of generating maximally general rules and evolving a completely converged population is investigated in detail. Furthermore, the paper approaches a first comparison with the XCS classifier system in different mazes and the multiplexer problem.
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