2D visualization of LCS rules learning to approximate a 3D function. Each blue ellipse represents an individual rule covering part of the solution space. (Adapted from images taken from XCSF[1] with permission from Martin Butz)
Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning).[2] Learning classifier systems seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions (e.g. behavior modeling,[3] classification,[4][5] data mining,[5][6][7] regression,[8] function approximation,[9] or game strategy). This approach allows complex solution spaces to be broken up into smaller, simpler parts.
The founding concepts behind learning classifier systems came from attempts to model complex adaptive systems, using rule-based agents to form an artificial cognitive system (i.e. artificial intelligence).
^Stalph, Patrick O.; Butz, Martin V. (2010-02-01). "JavaXCSF: The XCSF Learning Classifier System in Java". SIGEVOlution. 4 (3): 16–19. doi:10.1145/1731888.1731890. ISSN 1931-8499. S2CID 16861908.
^Urbanowicz, Ryan J.; Moore, Jason H. (2009-09-22). "Learning Classifier Systems: A Complete Introduction, Review, and Roadmap". Journal of Artificial Evolution and Applications. 2009: 1–25. doi:10.1155/2009/736398. ISSN 1687-6229.
^Dorigo, Marco (1995). "Alecsys and the AutonoMouse: Learning to control a real robot by distributed classifier systems". Machine Learning. 19 (3): 209–240. doi:10.1007/BF00996270. ISSN 0885-6125.
^Bernadó-Mansilla, Ester; Garrell-Guiu, Josep M. (2003-09-01). "Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks". Evolutionary Computation. 11 (3): 209–238. doi:10.1162/106365603322365289. ISSN 1063-6560. PMID 14558911. S2CID 9086149.
^ abUrbanowicz, Ryan J.; Moore, Jason H. (2015-04-03). "ExSTraCS 2.0: description and evaluation of a scalable learning classifier system". Evolutionary Intelligence. 8 (2–3): 89–116. doi:10.1007/s12065-015-0128-8. ISSN 1864-5909. PMC 4583133. PMID 26417393.
^Bernadó, Ester; Llorà, Xavier; Garrell, Josep M. (2001-07-07). "XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining". In Lanzi, Pier Luca; Stolzmann, Wolfgang; Wilson, Stewart W. (eds.). Advances in Learning Classifier Systems. Lecture Notes in Computer Science. Vol. 2321. Springer Berlin Heidelberg. pp. 115–132. doi:10.1007/3-540-48104-4_8. ISBN 9783540437932.
^Bacardit, Jaume; Butz, Martin V. (2007-01-01). "Data Mining in Learning Classifier Systems: Comparing XCS with GAssist". In Kovacs, Tim; Llorà, Xavier; Takadama, Keiki; Lanzi, Pier Luca; Stolzmann, Wolfgang; Wilson, Stewart W. (eds.). Learning Classifier Systems. Lecture Notes in Computer Science. Vol. 4399. Springer Berlin Heidelberg. pp. 282–290. CiteSeerX 10.1.1.553.4679. doi:10.1007/978-3-540-71231-2_19. ISBN 9783540712305.
^Urbanowicz, Ryan; Ramanand, Niranjan; Moore, Jason (2015-01-01). "Continuous Endpoint Data Mining with ExSTraCS". Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation. GECCO Companion '15. New York, NY, USA: ACM. pp. 1029–1036. doi:10.1145/2739482.2768453. ISBN 9781450334884. S2CID 11908241.
^Butz, M. V.; Lanzi, P. L.; Wilson, S. W. (2008-06-01). "Function Approximation With XCS: Hyperellipsoidal Conditions, Recursive Least Squares, and Compaction". IEEE Transactions on Evolutionary Computation. 12 (3): 355–376. doi:10.1109/TEVC.2007.903551. ISSN 1089-778X. S2CID 8861046.
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