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Data classification using genetic parallel programming
Date Issued
2003
Publisher
Springer Verlag
ISBN
978-354040603-7
Citation
Genetic and Evolutionary Computation Conference, 2003, pp. 1918 - 1919.
Type
Conference Paper
Abstract
A novel Linear Genetic Programming (LGP) paradigm called Genetic Parallel Programming (GPP) has been proposed to evolve parallel programs based on a Multi-ALU Processor. It is found that GPP can evolve parallel programs for Data Classification problems. In this paper, five binary-class UCI Machine Learning Repository databases are used to test the effectiveness of the proposed GPP-classifier. The main advantages of employing GPP for data classification are: 1) speeding up evolutionary process by parallel hardware fitness evaluation; and 2) discovering parallel algorithms automatically. Experimental results show that the GPP-classifier evolves simple classification programs with good generalization performance. The accuracies of these evolved classifiers are comparable to other existing classification algorithms. © Springer-Verlag Berlin Heidelberg 2003.
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