Genetic Programming for Classification: An Analysis of Convergence Behaviour

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Thomas Loveard and Vic Ciesielski",
  title =        "Genetic Programming for Classification: An Analysis of
                 Convergence Behaviour",
  volume =       "2557",
  pages =        "309--320",
  year =         "2002",
  CODEN =        "LNCSD9",
  ISSN =         "0302-9743",
  bibdate =      "Sat Nov 30 20:58:15 MST 2002",
  acknowledgement = ack-nhfb,
  booktitle =    "AI 2002: Advances in Artificial Intelligence : 15th
                 Australian Joint Conference on Artificial
  editor =       "Bob McKay and John Slaney",
  series =       "Lecture Notes in Computer Science",
  address =      "Canberra, Australia",
  month =        "2-6 " # dec,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-00197-2",
  URL =          "",
  DOI =          "doi:10.1007/3-540-36187-1_27",
  abstract =     "This paper investigates the unexpected convergence
                 behaviour of genetic Programming (GP) for
                 classification problems. Firstly the paper investigates
                 the relationship between computational effort and
                 attainable classification accuracy. Secondly we attempt
                 to understand why GP classifiers sometimes fail to
                 reach satisfactory levels of accuracy for certain
                 problems regardless of computational effort. The
                 investigation uses an artificially generated dataset
                 for which certain properties are known in advance for
                 the exploration of these areas. Results from this
                 artificial problem show that by increasing
                 computational effort, in the form of larger population
                 sizes and more generations, the probability of success
                 for a run does improve, but that the computational cost
                 far outweighs the rate of this success. Also, some
                 runs, even with very large populations running for many
                 generations, became stagnant and were unable to find an
                 acceptable solution. These results are also reflected
                 in real world classification problems.

                 From analysis of sub-tree components making up
                 successful and unsuccessful programs it was noted that
                 a small number of particular components were almost
                 always present in successful programs, and that these
                 components were often absent from unsuccessful
                 programs. Also a variety of components appeared in
                 unsuccessful programs that were never present in
                 successful ones. Evidence from runs suggests that these
                 components represent paths leading to optimal and
                 sub-optimal branches in the evolutionary search space.
                 Additionally, results suggest that if sub-optimal
                 components (which mirror the concept of deception in
                 genetic algorithms) are relatively greater in number
                 than the optimal components for the problem, then the
                 chances of GP finding a successful solution are

Genetic Programming entries for Thomas Loveard Victor Ciesielski