Abstract
Learning Classifier Systems (LCSs) are increasingly being found to be effective machine learning systems that can address a variety of real world problems (as testified by several chapters in this book). Based on seminal ideas due to Holland (1976, 1980), they gradually evolve rulesets, which either model a static dataset, or model actions (and chains of actions) in an environment. Comprehensive tutorial and survey material on this rapidly growing field is now provided in many places, but we particularly point to Holland et al (2000), Holland (2000) and Lanzi and Riolo (2000), as well as the introductory material in this volume.
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Dixon, P.W., Corne, D.W., Oates, M.J. (2004). Encouraging Compact Rulesets from XCS for Enhanced Data Mining. In: Bull, L. (eds) Applications of Learning Classifier Systems. Studies in Fuzziness and Soft Computing, vol 150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39925-4_4
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DOI: https://doi.org/10.1007/978-3-540-39925-4_4
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