|
Abstract:
|
Since the first Learning Classifier System (LCS) was introduced by Holland and Reitman in 1978, the LCS paradigm has broadened greatly into a framework encompassing many representations, rule discovery mechanisms, and credit assignment schemes. Current LCS applications range from data mining to automated innovation to on-line control. Classifier systems are currently enjoying a renaissance, with newer approaches, in particular Wilson's accuracy-based XCS, receiving a great deal of attention. LCS are also benefiting from advances in the field of Reinforcement Learning, and there is a trend toward developing connections between the two areas. The tutorial begins with an introduction to the basics of classifier systems, reviews more advanced techniques such as niche genetic algorithms and macroclassifiers, introduces XCS and contrasts it with earlier systems.
|
