Genetic Programming with Local Hill-Climbing

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{iba:1994:GPlHCppsn3,
  author =       "Hitoshi Iba and Hugo {de Garis} and Taisuke Sato",
  title =        "Genetic Programming with Local Hill-Climbing",
  booktitle =    "Parallel Problem Solving from Nature III",
  year =         "1994",
  editor =       "Yuval Davidor and Hans-Paul Schwefel and 
                 Reinhard M{\"a}nner",
  series =       "LNCS",
  volume =       "866",
  pages =        "334--343",
  address =      "Jerusalem",
  publisher_address = "Berlin, Germany",
  month =        "9-14 " # oct,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-58484-6",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6",
  DOI =          "doi:10.1007/3-540-58484-6_274",
  size =         "10 pages",
  abstract =     "This paper proposes a new approach to Genetic
                 Programming (GP). In traditional GP, recombination can
                 cause frequent disruption of building-blocks or
                 mutation can cause abrupt changes in the semantics. To
                 overcome these difficulties, we supplement traditional
                 GP with a recovery mechanism of disrupted
                 building-blocks. More precisely, we integrate the
                 structural search of traditional GP with a local
                 hill-climbing search, using a relabeling procedure.
                 This integration allows us to extend GP for Boolean and
                 numerical problems. We demonstrate the superior
                 effectiveness of our approach with experiments in
                 Boolean concept formation and symbolic regression.",
  notes =        "'We demonstrate the superior effectiveness of GP+local
                 Hill Climbing with experiments in Boolean concept
                 formation and symbolic regression'. Boolean GP combines
                 GP with Adaptive Logic Network trees. Combination can
                 evove to cope with time varying fitness functions.
                 Numerical GP combines GP with GMDH (Group Method of
                 Data Handling, Ivakhnenko)

                 PPSN3 see also technical note \cite{Iba:1994:GPlHC}",
}

Genetic Programming entries for Hitoshi Iba Hugo de Garis Taisuke Sato

Citations