Genetic Programming of Minimal Neural Nets Using Occam's Razor

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Byoung-Tak Zhang and Heinz M{\"u}hlenbein",
  title =        "Genetic Programming of Minimal Neural Nets Using
                 {O}ccam's Razor",
  year =         "1993",
  booktitle =    "Proceedings of the 5th International Conference on
                 Genetic Algorithms, ICGA-93",
  publisher =    "Morgan Kaufmann",
  editor =       "Stephanie Forrest",
  pages =        "342--349",
  address =      "University of Illinois at Urbana-Champaign",
  month =        "17-21 " # jul,
  URL =          "",
  keywords =     "genetic algorithms, genetic programming",
  size =         "8 pages",
  abstract =     "A genetic programming method is investigated for
                 optimizing both the architecture and the connection
                 weights of multilayer feedforward neural networks. The
                 genotype of each network is represented as a tree whose
                 depth and width are dynamically adapted to the
                 particular application by specifically defined genetic
                 operators. The weights are trained by a next-ascent
                 hillclimbing search. A new fitness function is proposed
                 that quantifies the principle of Occam's razor. It
                 makes an optimal trade-off between the error fitting
                 ability and the parsimony of the network. We discuss
                 the results for two problems of differing complexity
                 and study the convergence and scaling properties of the
  notes =        "GP feedforward binary ANN",

Genetic Programming entries for Byoung-Tak Zhang Heinz Muhlenbein