Simultaneous Discovery of Reusable Detectors and Subroutines Using Genetic Programming

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

@InProceedings{koza:adf,
  author =       "John R. Koza",
  title =        "Simultaneous Discovery of Reusable Detectors and
                 Subroutines Using Genetic Programming",
  editor =       "Stephanie Forrest",
  publisher_address = "San Mateo, CA, USA",
  year =         "1993",
  booktitle =    "Proceedings of the 5th International Conference on
                 Genetic Algorithms, ICGA-93",
  publisher =    "Morgan Kaufmann",
  size =         "8 pages",
  pages =        "295--302",
  address =      "University of Illinois at Urbana-Champaign",
  month =        "17-21 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/jkpdf/icga1993.pdf",
  abstract =     "This paper describes an approach for automatically
                 decomposing a problem into subproblems and then
                 automatically discovering reusable subroutines, and a
                 way of assembling the results produced by these
                 subroutines in order to solve a problem. The approach
                 uses genetic programming with automatic function
                 definition. Genetic programming provides a way to
                 genetically breed a computer program to solve a
                 problem. Automatic function definition enables genetic
                 programming to define potentially useful subroutines
                 dynamically during a run. The approach is applied to an
                 illustrative problem. Genetic programming with
                 automatic function definition reduced the computational
                 effort required to learn a solution to the problem by a
                 factor of 2.0 as compared to genetic programming
                 without automatic function definition. Similarly, the
                 average structural complexity of the solution was
                 reduced by about 21percent.",
  notes =        "Comparison of GP and GP+Automatic Function definition
                 for San Mateo trail ants, finds improvement of 1:2 in
                 number of fitness cases required and 21% reduction is
                 size of eventual s-expressions. NO CASE made that
                 either cases are using optimal parameters.",
}

Genetic Programming entries for John Koza

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