Robust GP in Robot Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Naohiro Hondo and Hitoshi Iba and Yukinori Kakazu",
  title =        "Robust GP in Robot Learning",
  booktitle =    "Parallel Problem Solving from Nature IV, Proceedings
                 of the International Conference on Evolutionary
  year =         "1996",
  editor =       "Hans-Michael Voigt and Werner Ebeling and 
                 Ingo Rechenberg and Hans-Paul Schwefel",
  series =       "LNCS",
  volume =       "1141",
  pages =        "751--760",
  address =      "Berlin, Germany",
  publisher_address = "Heidelberg, Germany",
  month =        "22-26 " # sep,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-61723-X",
  DOI =          "doi:10.1007/3-540-61723-X_1038",
  size =         "10 pages",
  abstract =     "This paper presents a new approach to Genetic
                 Programming (i.e. GP). Our goal is to realise
                 robustness by means of the automatic discovery of
                 functions. In traditional GP, techniques have been
                 proposed which attempt to discover certain subroutines
                 for the sake of improved efficiency. So far, however,
                 the robustness of GP has not yet been discussed in
                 terms of knowledge acquisition. We propose an approach
                 for robustness named COAST, which has a library for
                 storing certain subroutines for reuse. We make use of
                 the Wall Following Problem to illustrate the efficiency
                 of this method.",
  notes =        " PPSN4
                 COAST, Wall following problem",
  affiliation =  "Hokkaido University Complex Systems Engineering,
                 Division of Systems and Information Engineering N-13
                 W-8, Sapporo 060 Hokkaido Japan N-13 W-8, Sapporo 060
                 Hokkaido Japan",

Genetic Programming entries for Naohiro Hondo Hitoshi Iba Yukinori Kakazu