Combining Gradient-Based With Evolutionary Online Learning: An Introduction to Learning Classifier Systems

Learning classifier systems (LCSs), introduced by John H. Holland in the 1970s, are rule-based evolutionary online learning systems that combine gradient-based rule evaluation with evolutionary-based rule structuring techniques. Since the introduction of the accuracy-based XCS classifier system by Stewart W. Wilson in 1995, LCSs showed to be flexible, online learning methods that are applicable to datamining, reinforcement learning, and function approximation problems. Comparisons showed that performance is competitive with state-of-the art machine learning algorithms, but the learning algorithms applied are usually more flexible and highly adaptive. Moreover, problem knowledge can be extracted easily. This tutorial provides a gentle introduction to LCSs and their general functioning. It then gives further details on the XCS classifier system and highlights various successful applications. In conclusion, promising future directions of LCS research and applications are discussed.