Probability Based Genetic Programming for Multiclass Object Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Will Smart and Mengjie Zhang",
  title =        "Probability Based Genetic Programming for Multiclass
                 Object Classification",
  institution =  "Computer Science, Victoria University of Wellington",
  year =         "2004",
  number =       "CS-TR-04-7",
  address =      "New Zealand",
  keywords =     "genetic algorithms, genetic programming, Probability
                 based genetic programming, Gaussian distribution,
                 overlap area, weighted distribution distance,
                 multiclass object classification",
  URL =          "",
  URL =          "",
  abstract =     "Instead of using predefined multiple thresholds to
                 form different regions in the program output space for
                 different classes, this approach uses probabilities of
                 different classes, derived from Gaussian distributions,
                 to construct the fitness function for classification.
                 Two fitness measures, overlap area and weighted
                 distribution distance, have been developed. The
                 approach is examined on three multiclass object
                 classification problems of increasing difficulty and
                 compared with a basic GP approach. The results suggest
                 that the new approach is more effective and more
                 efficient than the basic GP approach. While the area
                 measure was a bit more effective than the distance
                 measure in most cases, the distance measure was more
                 efficient to learn good program classifiers.",

Genetic Programming entries for Will Smart Mengjie Zhang