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. The results clearly show that linkage exists in the multiplexer problem which needs to be accurately discovered and efficiently processed in order to solve the problem in tractable time. The results also show that in accordance with the facetwise models from GA theory, the number of function evaluations required by χeCCs to successfully evolve an optimal rule set scales exponentially with the number of address bits (building block size) and quadratically with the problem size. ...

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. Thus, artificial evolution can be easily modeled in terms of neighborhood relations. These neighborhood relations define GALE as a massive parallel evolutionary model. On the other hand, GALE does not constrain the knowledge representation. It can evolve indistinctly rules, instances, partially defined instances, and decision trees (orthogonal, oblique, and multivariate based on nearest neighbor). ...

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. Using a common rule encoding schemes of Pittsburgh-style classifier systems, CCS mantains a dynamic set of probability vectors that compactly describe a rule set. The compact genetic algorithm is used to evolve each of the initially perturbated probability vectors. Results show how CCS is able to evolve in a compact, simple, and elegant manner rule sets composed by maximally general and accurate rules. The initial theoretical analysis and results also show that traditional encoding schemes used by Pittsburgh-style classifiers add an extra facet of diffiiculty. Such a bias plays a central role on the overall performance and scalability of CCS and other Pittsburgh-style systems using such encoding schemes. ...