Discovery of a main program and reusable subroutines using genetic programming

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

@InProceedings{Koza:1993:mprsGP,
  author =       "John R. Koza",
  title =        "Discovery of a main program and reusable subroutines
                 using genetic programming",
  booktitle =    "Proceedings of the Fifth Workshop on Neural Networks:
                 An International Conference on Computational
                 Intelligence: Neural Networks, Fuzzy Systems,
                 Evolutionary Programming, and Virtual Reality",
  year =         "1993",
  pages =        "109--118",
  publisher_address = "San Diego, CA, USA",
  organisation = "The Society for Computer Simulation",
  keywords =     "genetic algorithms, genetic programming, ADF",
  URL =          "http://www.genetic-programming.com/jkpdf/simtec1993.pdf",
  oai =          "oai:CiteSeerXPSU:10.1.1.140.5520",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.5520",
  size =         "11 pages",
  abstract =     "This paper describes an approach for automatically
                 decomposing a problem into subproblems, automatically
                 creating reusable subroutines to solve the subproblems,
                 and automatically assembling the results produced by
                 the subroutines in order to solve the problem. The
                 approach uses genetic programming with the recently
                 developed additional facility of automatic function
                 definition. Genetic programming provides a way to
                 genetically breed a computer program to solve a problem
                 and automatic function definition enables genetic
                 programming to create reusable subroutines dynamically
                 during a run. The approach is applied to an
                 illustrative problem containing a considerable amount
                 of regularity. Solutions to the problem produced using
                 automatic function definition are considerably smaller
                 in size and require processing of considerably fewer
                 individuals than is the case without automatic function
                 definition. Specifically, the average program size for
                 a solution to the problem without using automatic
                 function definition is 3.65 times larger than the size
                 for a solution when using automatic function
                 definition. The number of individuals required to be
                 processed to yield a solution with 99% probability
                 without automatic function definition is 9.09 times
                 larger than the equivalent number required with
                 automatic function definition.",
}

Genetic Programming entries for John Koza

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