Linkage learning, rule representation, and the χ-ary extended compact classifier system
6 April 2008Llorà, X., Sastry, K., Lima, C. F., Lobo, F. G., Goldberg, D. E. (2008). IlliGAL Report No. 2008005. University of Illinois at Urbana-Champaign, Urbana IL. [Full Paper - PDF] [Full Paper - PS].
Abstract:
This paper reviews a competent Pittsburgh LCS that automatically mines important substructures of the underlying problems and takes problems that were intractable with first-generation Pittsburgh LCS and renders them tractable. Specifically, we propose a ?-ary extended compact classifier system which uses (1) a competent genetic algorithm (GA) in the form of ?-ary extended compact genetic algorithm, and (2) a niching method in the form restricted tournament replacement, to evolve a set of maximally accurate and maximally general rules. Besides showing that linkage exist on the multiplexer problem, and that ?eCCS scales exponentially with the number of address bits (building block size) and quadratically with the problem size, this paper also explores non-traditional rule encodings. Gene expression encodings, such as the Karva language, can also be used to build ?eCCS probabilistic models. However, results show that the traditional ternary encoding { 0,1,#} presents a better scalability than the gene expression inspired ones.
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