Cheang, Sin ManSin ManCheangLee, Kin HongKin HongLeeProf. LEUNG Kwong Sak2023-03-282023-03-2820032003 Congress on Evolutionary Computation, CEC 2003 - Proceedings, 2003, Vol. 1, pp. 248 - 255http://hdl.handle.net/20.500.11861/7635A 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. © 2003 IEEE.enGene Regulatory NetworksParallelizationClassification AlgorithmsGeneralization PerformanceGood GeneralizationGood Generalization PerformanceTraining SetDecision TreeNonlinear FunctionPopulation Of IndividualsComputer ProgramIndividual ProgramsTraining SubsetsExperiments In This PaperComplete DatabaseGeneral IntegrationValidation SubsetInput AttributesConditional BranchesConference Paper10.1109/CEC.2003.1299582