Illinois Genetic Algorithms Laboratory

Butz, M.V., Goldberg, D.E., Stolzmann, W. (2000)
TR No.: 2000006 | Download PDF | Download PS

Abstract:

The Anticipatory Classifier System (ACS) is able to form a complete internal representation of an environment. Unlike most other classifier 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:Technical Report 2000005). This paper focuses on the different mechanisms in the anticipatory learning process, which resembles the specification 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.