Abstract
Since its introduction Holland’s Learning Classifier System (LCS) [Holland, 1976] has inspired much research into ‘genetics-based’ machine learning [Goldberg, 1989]. Given the complexity of the developed system [Holland, 1986], simplified versions have previously been presented (e.g., [Goldberg, 1989][Wilson, 1994]) to improve both performance and understanding. It has recently been shown that Wilson’s simpler ‘zeroth-level’ system (ZCS) [Wilson, 1994] can perform optimally [Bull & Hurst, 2002] but “it would appear that the interaction between the rate of rule updates and the fitness sharing process is critical” [ibid.]. In this chapter, a simplified version of ZCS is explored - termed a ‘minimal’ classifier system, MCS.
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Bull, L. Two Simple Learning Classifier Systems. In: Bull, L., Kovacs, T. (eds) Foundations of Learning Classifier Systems. Studies in Fuzziness and Soft Computing, vol 183. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11319122_4
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DOI: https://doi.org/10.1007/11319122_4
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