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- W2106869863 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." @default.
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- W2106869863 date "2004-07-09" @default.
- W2106869863 modified "2023-09-25" @default.
- W2106869863 title "Evolving data classification programs using genetic parallel programming" @default.
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- W2106869863 doi "https://doi.org/10.1109/cec.2003.1299582" @default.
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