Evolving data classification programs using genetic parallel programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{cheang:2003:edcpugpp,

• author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung",
• title = "Evolving data classification programs using genetic parallel programming",
• booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003",
• editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon",
• pages = "248--255",
• year = "2003",
• publisher = "IEEE Press",
• address = "Canberra",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• month = "8-12 " # dec,
• organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
• 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",
• ISBN = "0-7803-7804-0",
• DOI = "doi:10.1109/CEC.2003.1299582",
• 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.",
• 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

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