%0 Conference Paper %1 cheang2:2003:gecco %A Cheang, Sin Man %A Lee, Kin Hong %A Leung, Kwong Sak %B Genetic and Evolutionary Computation -- GECCO-2003 %C Chicago %D 2003 %E Cantú-Paz, E. %E Foster, J. A. %E Deb, K. %E Davis, D. %E Roy, R. %E O'Reilly, U.-M. %E Beyer, H.-G. %E Standish, R. %E Kendall, G. %E Wilson, S. %E Harman, M. %E Wegener, J. %E Dasgupta, D. %E Potter, M. A. %E Schultz, A. C. %E Dowsland, K. %E Jonoska, N. %E Miller, J. %I Springer-Verlag %K Classifier Learning Systems, algorithms, genetic poster programming, %P 1918--1919 %T Data Classification Using Genetic Parallel Programming %V 2724 %X 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. %@ 3-540-40603-4

@inproceedings{cheang2:2003:gecco, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Chicago}, author = {Cheang, Sin Man and Lee, Kin Hong and Leung, Kwong Sak}, biburl = {https://www.bibsonomy.org/bibtex/24558af8706662827c093743630b45f55/brazovayeye}, booktitle = {Genetic and Evolutionary Computation -- GECCO-2003}, editor = {Cant{\'u}-Paz, E. and Foster, J. A. and Deb, K. and Davis, D. and Roy, R. and O'Reilly, U.-M. and Beyer, H.-G. and Standish, R. and Kendall, G. and Wilson, S. and Harman, M. and Wegener, J. and Dasgupta, D. and Potter, M. A. and Schultz, A. C. and Dowsland, K. and Jonoska, N. and Miller, J.}, interhash = {f4b3c27f5f500d55b01dd0f8ca9bf8f5}, intrahash = {4558af8706662827c093743630b45f55}, isbn = {3-540-40603-4}, keywords = {Classifier Learning Systems, algorithms, genetic poster programming,}, month = {12-16 July}, notes = {GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)}, pages = {1918--1919}, publisher = {Springer-Verlag}, publisher_address = {Berlin}, series = {LNCS}, timestamp = {2008-06-19T17:37:36.000+0200}, title = {Data Classification Using Genetic Parallel Programming}, volume = 2724, year = 2003 }