<sod>'''Learning classifier systems''', or '''LCS''', are a family 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]])<ref>{{Cite journal|last=Urbanowicz|first=Ryan J.|last2=Moore|first2=Jason H.|date=2009-09-22|title=Learning Classifier Systems: A Complete Introduction, Review, and Roadmap|url=http://www.hindawi.com/archive/2009/736398/|journal=Journal of Artificial Evolution and Applications|language=en|volume=2009|pages=1–25|doi=10.1155/2009/736398|issn=1687-6229}}</ref>.  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]], [[classification]], [[data mining]], [[Strategy (game theory)|game strategy]], or [[function approximation]]).  This approach allows complex [[Feasible region|solution spaces]] to be broken up into simpler parts.  <eod>
<sod>'''Learning classifier systems''', or '''LCS''', are a family 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]])<ref>{{Cite journal|last=Urbanowicz|first=Ryan J.|last2=Moore|first2=Jason H.|date=2009-09-22|title=Learning Classifier Systems: A Complete Introduction, Review, and Roadmap|url=http://www.hindawi.com/archive/2009/736398/|journal=Journal of Artificial Evolution and Applications|language=en|volume=2009|pages=1–25|doi=10.1155/2009/736398|issn=1687-6229}}</ref>.  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]]<ref>{{Cite journal|last=Dorigo|first=Marco|title=Alecsys and the AutonoMouse: Learning to control a real robot by distributed classifier systems|url=http://link.springer.com/article/10.1007/BF00996270|journal=Machine Learning|language=en|volume=19|issue=3|pages=209–240|doi=10.1007/BF00996270|issn=0885-6125}}</ref>, [[classification]]<ref>{{Cite journal|last=Bernadó-Mansilla|first=Ester|last2=Garrell-Guiu|first2=Josep M.|date=2003-09-01|title=Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks|url=http://dx.doi.org/10.1162/106365603322365289|journal=Evolutionary Computation|volume=11|issue=3|pages=209–238|doi=10.1162/106365603322365289|issn=1063-6560}}</ref><ref name=":0">{{Cite journal|last=Urbanowicz|first=Ryan J.|last2=Moore|first2=Jason H.|date=2015-04-03|title=ExSTraCS 2.0: description and evaluation of a scalable learning classifier system|url=http://link.springer.com/article/10.1007/s12065-015-0128-8|journal=Evolutionary Intelligence|language=en|volume=8|issue=2-3|pages=89–116|doi=10.1007/s12065-015-0128-8|issn=1864-5909|pmc=4583133|pmid=26417393}}</ref>, [[data mining]]<ref>{{Cite book|url=http://link.springer.com/chapter/10.1007/3-540-48104-4_8|title=Advances in Learning Classifier Systems|last=Bernadó|first=Ester|last2=Llorà|first2=Xavier|last3=Garrell|first3=Josep M.|date=2001-07-07|publisher=Springer Berlin Heidelberg|isbn=9783540437932|editor-last=Lanzi|editor-first=Pier Luca|series=Lecture Notes in Computer Science|pages=115–132|language=en|doi=10.1007/3-540-48104-4_8|editor-last2=Stolzmann|editor-first2=Wolfgang|editor-last3=Wilson|editor-first3=Stewart W.}}</ref><ref>{{Cite book|url=http://link.springer.com/chapter/10.1007/978-3-540-71231-2_19|title=Learning Classifier Systems|last=Bacardit|first=Jaume|last2=Butz|first2=Martin V.|date=2007-01-01|publisher=Springer Berlin Heidelberg|isbn=9783540712305|editor-last=Kovacs|editor-first=Tim|series=Lecture Notes in Computer Science|pages=282–290|language=en|doi=10.1007/978-3-540-71231-2_19|editor-last2=Llorà|editor-first2=Xavier|editor-last3=Takadama|editor-first3=Keiki|editor-last4=Lanzi|editor-first4=Pier Luca|editor-last5=Stolzmann|editor-first5=Wolfgang|editor-last6=Wilson|editor-first6=Stewart W.}}</ref><ref name=":0" />, [[Regression analysis|regression]]<ref>{{Cite journal|last=Urbanowicz|first=Ryan|last2=Ramanand|first2=Niranjan|last3=Moore|first3=Jason|date=2015-01-01|title=Continuous Endpoint Data Mining with ExSTraCS: A Supervised Learning Classifier System|url=http://doi.acm.org/10.1145/2739482.2768453|journal=Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation|series=GECCO Companion '15|location=New York, NY, USA|publisher=ACM|pages=1029–1036|doi=10.1145/2739482.2768453|isbn=9781450334884}}</ref>, [[function approximation]]<ref>{{Cite journal|last=Butz|first=M. V.|last2=Lanzi|first2=P. L.|last3=Wilson|first3=S. W.|date=2008-06-01|title=Function Approximation With XCS: Hyperellipsoidal Conditions, Recursive Least Squares, and Compaction|url=http://ieeexplore.ieee.org/document/4380293/|journal=IEEE Transactions on Evolutionary Computation|volume=12|issue=3|pages=355–376|doi=10.1109/TEVC.2007.903551|issn=1089-778X}}</ref>, or [[Strategy (game theory)|game strategy]]).  This approach allows complex [[Feasible region|solution spaces]] to be broken up into simpler parts.  <eod>