2003 | OriginalPaper | Buchkapitel
Data Classification Using Genetic Parallel Programming
verfasst von : Sin Man Cheang, Kin Hong Lee, Kwong Sak Leung
Erschienen in: Genetic and Evolutionary Computation — GECCO 2003
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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.