Data Classification Using Genetic Parallel Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung",
  title =        "Data Classification Using Genetic Parallel
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2003",
  editor =       "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and 
                 D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and 
                 R. Standish and G. Kendall and S. Wilson and 
                 M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and 
                 A. C. Schultz and K. Dowsland and N. Jonoska and 
                 J. Miller",
  year =         "2003",
  pages =        "1918--1919",
  address =      "Chicago",
  publisher_address = "Berlin",
  month =        "12-16 " # jul,
  volume =       "2724",
  series =       "LNCS",
  ISBN =         "3-540-40603-4",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Learning
                 Classifier Systems, poster",
  DOI =          "doi:10.1007/3-540-45110-2_88",
  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.",
  notes =        "GECCO-2003. A joint meeting of the twelfth
                 International Conference on Genetic Algorithms
                 (ICGA-2003) and the eighth Annual Genetic Programming
                 Conference (GP-2003)",

Genetic Programming entries for Ivan Sin Man Cheang Kin-Hong Lee Kwong-Sak Leung