Issues in Evolving GP based Classifiers for a Pattern Recognition Task

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

  title =        "Issues in Evolving GP based Classifiers for a Pattern
                 Recognition Task",
  author =       "Ankur Teredesai and Venu Govindaraju",
  pages =        "509--515",
  booktitle =    "Proceedings of the 2004 IEEE Congress on Evolutionary
  year =         "2004",
  publisher =    "IEEE Press",
  month =        "20-23 " # jun,
  address =      "Portland, Oregon",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Real-world
  URL =          "",
  DOI =          "doi:10.1109/CEC.2004.1330899",
  abstract =     "This paper discusses issues when evolving Genetic
                 Programming (GP) classifiers for a pattern recognition
                 task such as handwritten digit recognition. Developing
                 elegant solutions for handwritten digit classification
                 is a challenging task. Similarly, design and training
                 of classifiers using genetic programming is a
                 relatively new approach in pattern recognition as
                 compared to other traditional techniques. Several
                 strategies for GP training are outlined and the
                 empirical observations are reported. The issues we
                 faced such as training time, a variety of fitness
                 landscapes and accuracy of results are discussed. Care
                 has been taken to test GP using a variety of parameters
                 and on several handwritten digits datasets.",
  notes =        "CEC 2004 - A joint meeting of the IEEE, the EPS, and
                 the IEE.",

Genetic Programming entries for Ankur M Teredesai Venugopal Govindaraju