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.
Keywords
- Classification Error
- Generalization Performance
- Data Classifier
- Good Generalization Performance
- Classification Error Rate
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Lim, T.S., Loh, W.Y., Shih, Y.S.: A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms. Machine Learning Journal, Vol.40, Kluwer Academic (2000) 203–229
Leung, K.S., Lee, K.H., Cheang, S.M.: Evolving Parallel Machine Programs for a Multi-ALU Processor. Proc. of IEEE Congress on Evolutionary Computation (2002) 1703–1708
Leung, K.S., Lee, K.H., Cheang, S.M.: Genetic Parallel Programming — Evolving Linear Machine Codes on a Multiple-ALU Processor. Proc. of International Conference on Intelligence in Engineering and Technology, Univ. Malaysia Sabah (2002) 207–213
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Cheang, S.M., Lee, K.H., Leung, K.S. (2003). Data Classification Using Genetic Parallel Programming. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_88
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DOI: https://doi.org/10.1007/3-540-45110-2_88
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