Learning classifier systems

Learning Classifier Systems are a machine learning technique that may be categorised in between symbolic production systems and sub-symbolic connectionist systems. Classifiers are cognitive paradigm for adaptation that learn in environments of perpetual novelty with minimal and delayed reward. They employ two principle processes: (1) reinforcement learning called ‘trial-and-error’, and (2) genetic evolution called ‘survival-of-the-fittest’. This work provides a brief review of classifier systems with a focus on the principles of the learning paradigm.