Evolving data classification programs using genetic parallel programming
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gp-bibliography.bib Revision:1.8098
- @InProceedings{cheang:2003:edcpugpp,
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author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung",
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title = "Evolving data classification programs using genetic
parallel programming",
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booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
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editor = "Ruhul Sarker and Robert Reynolds and
Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and
Tom Gedeon",
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pages = "248--255",
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year = "2003",
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publisher = "IEEE Press",
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address = "Canberra",
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publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
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month = "8-12 " # dec,
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organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
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keywords = "genetic algorithms, genetic programming, Acceleration,
Classification algorithms, Concurrent computing, Data
mining, Databases, Machine learning, Machine learning
algorithms, Parallel programming, Registers, data
analysis, learning (artificial intelligence), parallel
programming, pattern classification, tree data
structures, GPP-classifier, UCI machine learning
repository databases, classification algorithms, data
classification problems, data classification programs,
evolutionary process, generalization performance,
genetic parallel programming, linear genetic
programming paradigm, multiALU processor, parallel
algorithms, parallel hardware fitness evaluation,
parallel programs",
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ISBN = "0-7803-7804-0",
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DOI = "doi:10.1109/CEC.2003.1299582",
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abstract = "A novel Linear Genetic Programming (Linear GP)
paradigm called Genetic Parallel Programming (GPP) has
been proposed to evolve parallel programs based on a
Multi-ALU Processor. The GPP Accelerating Phenomenon,
i.e. parallel programs are easier to be evolved than
sequential programs, opens up a new two-step approach:
1) evolves a parallel program solution; and 2)
serialises the parallel program to a equivalent
sequential program. In this paper, five two-class UCI
Machine Learning Repository databases are used to
investigate the effectiveness of GPP. The main
advantages to employ GPP for data classification are:
1) speeding up evolutionary process by parallel
hardware fitness evaluation; 2) discovering parallel
algorithms automatically; and 3) boosting evolutionary
performance by the GPP Accelerating Phenomenon.
Experimental results show that GPP evolves simple
classification programs with good generalisation
performance. The accuracies of these evolved
classification programs are comparable to other
existing classification algorithms.",
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notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
- }
Genetic Programming entries for
Ivan Sin Man Cheang
Kin-Hong Lee
Kwong-Sak Leung
Citations