Learning classifier systems
Pages 941 - 962
Abstract
In the 1970s, John H. Holland designed Learning Classifier Systems (LCSs) as highly adaptive, cognitive systems. Since the introduction of the accuracy-based XCS classifier system by Stewart W. Wilson in 1995 and the modular analysis of several LCSs thereafter, LCSs have become a state-of-the-art machine learning system. Various publications have shown that LCSs can effectively solve data-mining problems, reinforcement learning problems, other predictive problems, and even cognitive, robotics control problems. In comparison to other, non-evolutionary machine learning techniques, it was shown that performance is competitive or even superior, dependent on the setup and problem. Advantages are that LCSs are learning online, are very plastic and flexible, are applicable to a larger range of problems, and are highly adaptive. Moreover, system knowledge can be easily extracted, visualized, or even used to focus the progressive search on particular interesting subspaces. The Learning Classifier System tutorial provides a gentle introduction to LCSs and their general functionality. It then surveys the current theoretical understanding of the systems. Finally, we provide a suite of current successful LCS applications and discuss the most promising areas for future applications and research directions.
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Published: 12 July 2011
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