Evolving data classification programs using genetic parallel programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  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