Introducing a Genetic Generalization Pressure to the Anticipatory Classiier System Part 2: Performance Analysis Introducing a Genetic Generalization Pressure to the Anticipatory Classiier System Part 2: Performance Analysis

The Anticipatory Classiier System (ACS) is able to form a complete internal representation of an environment. Unlike most other classiier system and reinforcement learning approaches, it is able to learn latently (i.e. to learn in an environment without getting any reward). Compared to other systems which are also able to form an internal representation of the outside world, the advantage of the ACS is that it is not forming an identical copy of the environment but it is generating a complete but more general model. After the observation that the model is not necessarily maximally general a genetic generalization pressure was introduced to the ACS (Butz, Goldberg, & Stolzmann, 2000). This paper focuses on the diierent mechanisms in the anticipatory learning process, which resembles the speciication 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 rst comparison with the XCS classiier system in diierent mazes and the multiplexer problem.