Performance improvement of machine learning via automatic discovery of facilitating functions as applied to a problem of symbolic system identification

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

@InProceedings{Koza:1993:pimlssi,
  author =       "John R. Koza and Martin A. Keane and James P. Rice",
  title =        "Performance improvement of machine learning via
                 automatic discovery of facilitating functions as
                 applied to a problem of symbolic system
                 identification",
  booktitle =    "1993 IEEE International Conference on Neural
                 Networks",
  year =         "1993",
  volume =       "I",
  pages =        "191--198",
  address =      "San Francisco, USA",
  publisher_address = "Piscataway, NJ, USA",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/jkpdf/icnn1993impulse.pdf",
  size =         "8 pages",
  abstract =     "The recently developed genetic programming paradigm
                 provides a way to genetically breed a population of
                 computer programs to solve problems. Automatic function
                 definition enables genetic programming to define
                 potentially useful functions dynamically during a run -
                 much as a human programmer writing a complex computer
                 program creates subroutines to perform certain groups
                 of steps which must be performed in more than one place
                 in the main program. This paper illustrates the value
                 of automatic function definition in enabling genetic
                 programming to accelerate the finding of an
                 approximation to the impulse response function, in
                 symbolic form, for a linear time-invariant system.",
}

Genetic Programming entries for John Koza Martin A Keane James P Rice