Recombination Guidance for Numerical Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{iba:1885:rgn,
  author =       "Hitoshi Iba and Taisuke Sato and Hugo {de Garis}",
  title =        "Recombination Guidance for Numerical Genetic
                 Programming",
  booktitle =    "1995 IEEE Conference on Evolutionary Computation",
  year =         "1995",
  volume =       "1",
  pages =        "97--102",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, adaptive
                 estimation, computer vision, numerical analysis, search
                 problems, statistical analysis, time series,
                 STROGANOFF, adaptive program, adaptive recombination
                 mechanism, chaotic time series prediction, genetic
                 algorithm-based search strategy, genetic program
                 recombination, multiple regression analysis method,
                 nonlinear function fitting, numerical genetic
                 programming, structured representation, system
                 identification problems",
  ISBN =         "0-7803-2759-4",
  doi =          "doi:10.1109/ICEC.1995.489292",
  size =         "6 pages",
  abstract =     "In our earlier papers, we introduced our adaptive
                 program called STROGANOFF (i.e. STructured
                 Representation On Genetic Algorithms for Non-linear
                 Function Fitting), which integrated a multiple
                 regression analysis method and a GA-based search
                 strategy. The effectiveness of STROGANOFF was
                 demonstrated by solving several system identification
                 problems. This paper proposes an {"}adaptive
                 recombination{"} mechanism for STROGANOFF. Our
                 intention is to exploit already built structures by
                 'adaptive recombination', in which GP recombination is
                 guided by a certain measure. The effectiveness of our
                 approach is shown by the experiment in predicting a
                 chaotic time series. Thereafter we describe real-world
                 applications of STROGANOFF to computer vision.",
  notes =        "ICEC-95 Editors not given by IEEE, Organisers David
                 Fogel and Chris deSilva.

                 conference details at
                 http://ciips.ee.uwa.edu.au/~dorota/icnn95.html

                 Female face outline. Stately home Hursley house
                 windows.",
}

Genetic Programming entries for Hitoshi Iba Taisuke Sato Hugo de Garis