Bayesian Inference, Minimum Description Length Principle, and Learning by Genetic Programming

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

@InProceedings{zhang:1995:bimdl,
  author =       "Byoung-Tak Zhang and Heinz M{\"u}hlenbein",
  title =        "Bayesian Inference, Minimum Description Length
                 Principle, and Learning by Genetic Programming",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "1--5",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/zhang_1995_bimdl.pdf",
  size =         "5 pages",
  abstract =     "adaptive search technique which dynamically balances
                 the ratio of training accuracy to complexity of
                 programs to achieve parsimonious solutions without
                 loosing population diversity.",
  notes =        "part of \cite{rosca:1995:ml}",
}

Genetic Programming entries for Byoung-Tak Zhang Heinz Muhlenbein

Citations