A basic classifier system, ZCS, is presented that keeps much of Holland's original framework but simplifies it to increase understandability and performance. ZCS's relation to Q-learning is brought out, and their performances compared in environments of two difficulty levels. Extensions to ZCS are proposed for temporary memory, better action selection, more efficient use of the genetic algorithm, and more general classifier representation.

Issue Section:
Articles
Classifier systems, Q-learning, temporary memory, action selection, restricted mating, s-classifiers, genetic programming
This content is only available as a PDF.
© 1994 by the Massachusetts Institute of Technology
1994
You do not currently have access to this content.