Data classification using genetic parallel programming

Authors:
Sin Man Cheang;Kin Hong Lee;Kwong Sak Leung
Affiliations:
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Year:
2003

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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.