Recombination Guidance for Numerical Genetic Programming   [GP]

by

Iba, H., Garis, H., D., Garis, H., . and Sato, T.

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Info: 1995 IEEE Conference on Evolutionary Computation (Conference proceedings), 1995, p. 97
Keywords:genetic algorithms, genetic programming
Abstract:
In our earlier papers, we introduced our adaptive program called "STROGANOFF" (i.e. STructured Representation On Genetic Algorithms [GA] 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. [SI] 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. [TS] Thereafter we describe real-world applications of STROGANOFF to computer vision. [CV]
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
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BibTex:
@InProceedings{iba:1885:rgn,
  author =       "Hitoshi Iba and Hugo {de Garis} and Taisuka Sato",
  title =        "Recombination Guidance for Numerical Genetic
                 Programming",
  booktitle =    "1995 IEEE Conference on Evolutionary Computation",
  year =         "1995",
  volume =       "1",
  pages =        "97",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  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

                 ",
}