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Data Classification Using Genetic Parallel Programming
Sin Man Cheang, Kin Hong Lee, and Kwong Sak Leung
Department of Computer Science and Engineering
The Chinese University of Hong Kong
Hong Kong
{smcheang,khlee,ksleung}@cse.cuhk.edu.hk
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.
LNCS 2724, p. 1918 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003