Classifier System Renaissance: New Analogies, New Directions

Abstract

Learning classifier systems (LCSs) have existed for nearly twenty years (Holland & Reitman, 1978). Research efforts in reinforcement learning (RL), evolutionary computation (EC), and neural networks have enhanced the original LCS paradigm. New thoughts from these areas have created a renaissance period for the LCS. This paper highlights some key LCS advancements and the fields that inspired them. One inspiration, from neural networks, is examined for a novel LCS approach to autonomous mobile robots. A simple, LCS-controlled robot simulation is presented. This simulation shows the potential benefits of combined biological paradigms and the hybridization of ideas in the LCS. Future directions for LCS research are discussed.