Investigating Generalization in the Anticipatory Classiier System Investigating Generalization in the Anticipatory Classiier System

Recently, a genetic algorithm (GA) was introduced to the Anticipatory Classiier System (ACS) which surmounted the occasional problem of over-speciication of rules. This paper investigates the resulting generalization capabilities further by monitoring in detail the performance of the ACS in the highly challenging multiplexer task. Moreover, by comparing the ACS to XCS in this task it is shown that the ACS generates accurate, maximally general rules and its population converges to those rules. Besides the observed ability of latent learning and the formation of an internal environmental representation, this ability of generalization adds a new advantage to the ACS in comparison with similar approaches.