Evolutionary Computation Framework for Learning from Visual Examples

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

  author =       "Krzysztof Krawiec",
  title =        "Evolutionary Computation Framework for Learning from
                 Visual Examples",
  journal =      "Image Processing and Communications",
  year =         "2001",
  volume =       "7",
  number =       "3-4",
  pages =        "85--96",
  keywords =     "genetic algorithms, genetic programming, visual
                 learning, genetic local search, learning from
  ISSN =         "1425-140X",
  URL =          "http://www-idss.cs.put.poznan.pl/~krawiec/pubs/ipc2001.pdf",
  URL =          "http://citeseer.ist.psu.edu/494563.html",
  size =         "13 pages",
  abstract =     "This paper investigates the use of evolutionary
                 programming for the search of hypothesis space in
                 visual learning tasks. The general goal of the project
                 is to elaborate human-competitive procedures for
                 pattern discrimination by means of learning based on
                 the training data (set of images). In particular, the
                 topic addressed here is the comparison between the
                 standard genetic programming (as defined by Koza [13])
                 and the genetic programming extended by local
                 optimisation of solutions, so-called genetic local
                 search. The hypothesis formulated in the paper is that
                 genetic local search provides better solutions (i.e.
                 classifiers with higher predictive accuracy) than the
                 genetic search without that extension. This supposition
                 was positively verified in an extensive comparative
                 experiment of visual learning concerning the
                 recognition of handwritten characters.",
  notes =        "http://wtie.atr.bydgoszcz.pl/ip&c/indexip&c.html",

Genetic Programming entries for Krzysztof Krawiec