Machine Learning

by Xavier Llorà, Kumara Sastry, David E. Goldberg, and Luis de la Ossa (2006). To appear in the Proceedings of the International Workshop on Learning Classifier Systems (IWLCS 2006). Also as IlliGAL TR No 2006015. Link to the PDF. Abstract This paper proposes 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 χ-ary extended compact classifier system (χeCCS) which uses (1) a competent genetic algorithm (GA) in the form of χ-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....

Yes, with the new update of the website GALE was unreachable for a while. Here it is the original code again. Please, take a few minutes to read this post. Hope you find it useful. Enjoy :D GALE? GALE (Genetic and Artificial Life Environment) is fine-grained parallel genetic algorithm for data mining. Its main contributions are simplicity and its knowledge-independent model. The simplicity of GALE relies in its fine-grained parallelism based on spreading the population (feasible solutions to the classification task) over a 2D grid....

by Xavier Llorà, Kumara Sastry, and David E. Goldberg (2006). Proceedings of the Congress on Evolutionary Computation, 1, 596—603. Also as IlliGAL TR No 2005019. Link to the PDF. Abstract This paper presents an analysis of how maximally general and accurate rules can be evolved in a Pittsburgh-style classifier system. In order to be able to perform such an analysis we introduce a simple bare-bones Pittsburgh-style classifier systems—the compact classifier system (CCS)—based on estimation of distribution algorithms....