Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7635
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cheang, Sin Man | en_US |
dc.contributor.author | Lee, Kin Hong | en_US |
dc.contributor.author | Prof. LEUNG Kwong Sak | en_US |
dc.date.accessioned | 2023-03-28T04:49:28Z | - |
dc.date.available | 2023-03-28T04:49:28Z | - |
dc.date.issued | 2003 | - |
dc.identifier.citation | 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings, 2003, Vol. 1, pp. 248 - 255 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.11861/7635 | - |
dc.description.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. © 2003 IEEE. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE Computer Society | en_US |
dc.relation.ispartof | 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings | en_US |
dc.title | Evolving data classification programs using genetic parallel programming | en_US |
dc.type | Conference Proceedings | en_US |
dc.identifier.doi | 10.1109/CEC.2003.1299582 | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Department of Applied Data Science | - |
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