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)
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).
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^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. CiteSeerX10.1.1.553.4679. doi:10.1007/978-3-540-71231-2_19. ISBN9783540712305.
^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. ISBN9781450334884. S2CID11908241.
^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. ISSN1089-778X. S2CID8861046.