Investigating Generalization in the Anticipatory Classifier System
TR No.: 2000014 | Download PDF | Download PS
Abstract:
Recently, a genetic algorithm (GA) was introduced to the Anticipatory Classifier System (ACS) which surmounted the occasional problem of over-specification 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.
Posted: March 12th, 2000 under Genetic algorithms.
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