Illinois Genetic Algorithms Laboratory

Butz, M., Goldberg, D.E., Stolzmann, W. (2000)
TR No.: 2000032 | Download PDF | Download PS

Abstract:
The anticipatory classifier system (ACS)
combines the learning classifier system framework with the learning theory of
anticipatory behavioral control. The result is an evolutionary system that
builds an environmental model and further applies reinforcement learning

techniques to form an optimal behavioral policy in the model. After providing
some background as well as outlining the objectives of the system, we explain
in detail all involved current processes. Furthermore, we analyze the deficiency of over-specialization
in the anticipatory learning process (ALP), the main learning mechanism in the ACS.
Consequently, we introduce a genetic algorithm (GA) to the ACS that is meant for
generalization of over-specialized classifiers. We show that it is possible to
form a symbiosis between a directed specialization and a genetic generalization
mechanism achieving a learning mechanism that evolves a complete, accurate, and compact description of a
perceived environment. Results in three different environmental settings
confirm the usefulness of the genetic algorithm in the ACS. Finally, we discuss
future research directions with the ACS and anticipatory systems in general.