Genetic Programming Using a Minimum Description Length Principle

Created by W.Langdon from gp-bibliography.bib Revision:1.1944

@InCollection{kinnear:iba,
  title =        "Genetic Programming Using a Minimum Description Length
                 Principle",
  author =       "Hitoshi Iba and Hugo {de Garis} and Taisuke Sato",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  pages =        "265--284",
  chapter =      "12",
  size =         "15 pages",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/etl-tr-93-15.pdf",
  URL =          "http://citeseer.ist.psu.edu/327857.html",
  URL =          "http://cognet.mit.edu/library/books/view?isbn=0262111888",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This paper introduces a Minimum Description Length
                 (MDL) principle to define fitness functions in Genetic
                 Programming (GP). In traditional (Koza-style) GP, the
                 size of trees was usually controlled by user-defined
                 parameters, such as the maximum number of nodes and
                 maximum tree depth. Large tree sizes meant that the
                 time necessary to measure their fitnesses often
                 dominated total processing time. To overcome this
                 difficulty, we introduce a method for controlling tree
                 growth, which uses an MDL principle. Initially we
                 choose a {"}decision tree{"} representation for the GP
                 chromosomes, and then show how an MDL principle can be
                 used to define GP fitness functions. Thereafter we
                 apply the MDL-based fitness functions to some practical
                 problems. Using our implemented system
                 {"}STROGANOFF{"}, we show how MDL-based fitness
                 functions can be applied successfully to problems of
                 pattern recognitions. The results demonstrate that our
                 approach is superior to usual neural networks in terms
                 of general...",
  notes =        "Describes MDL; Work on both decision trees and GMDH
                 symbolic regression trees (STROGANOFF). Nature of trees
                 (ie never worse than component trees) more important
                 than MDL?

                 ",
}

Genetic Programming entries for Hitoshi Iba Hugo de Garis Taisuke Sato