Using Gaussian Distribution to Construct Fitness Functions in Genetic Programming for Multiclass Object Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@TechReport{vuw-CS-TR-05-5,
  author =       "Mengjie Zhang and Will Smart",
  title =        "Using Gaussian Distribution to Construct Fitness
                 Functions in Genetic Programming for Multiclass Object
                 Classification",
  institution =  "Computer Science, Victoria University of Wellington",
  year =         "2005",
  number =       "CS-TR-05-5",
  address =      "New Zealand",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-05-5.abs.html",
  URL =          "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-05/CS-TR-05-5.pdf",
  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. Rather than
                 using the best evolved program in a population, this
                 approach uses multiple programs and a voting strategy
                 to perform classification. 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. Although developed for object classification,
                 this approach is expected to be able to be applied to
                 other classification problems.",
}

Genetic Programming entries for Mengjie Zhang Will Smart

Citations