Cheang, Sin ManSin ManCheangLee, Kin HongKin HongLeeProf. LEUNG Kwong Sak2023-03-282023-03-282003Genetic and Evolutionary Computation Conference, 2003, pp. 1918 - 1919.978-354040603-7http://hdl.handle.net/20.500.11861/7634A 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.enClassification ErrorGeneralization PerformanceData ClassifierGood Generalization PerformanceClassification Error RateConference Paper10.1007/3-540-45110-2_88