Abstract
Several binary rule encoding schemes have been proposed for Pittsburgh-style classifier systems. This paper focus on the analysis of how maximally general and accurate rules, regardless of the encoding, can be evolved in a such classifier systems. The theoretical analysis of maximally general and accurate rules using two different binary rule encoding schemes showed some theoretical results with clear implications to the scalability of any genetic-based machine learning system that uses the studied encoding schemes. Such results are clearly relevant since one of the binary representations studied is widely used on Pittsburgh-style classifier systems, and shows an exponential shrink of the useful rules available as the problem size increases . In order to be able to perform such analysis we use a simple barebones Pittsburgh classifier system—the compact classifier system (CCS)—based on estimation of distribution algorithms.
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Llorà, X., Sastry, K., Goldberg, D.E. (2007). Binary Rule Encoding Schemes: A Study Using the Compact Classifier System. In: Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS IWLCS IWLCS 2003 2004 2005. Lecture Notes in Computer Science(), vol 4399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71231-2_4
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