A case study where biology inspired a solution to a computer science problem

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "John R. Koza and David Andre",
  title =        "A case study where biology inspired a solution to a
                 computer science problem",
  booktitle =    "Pacific Symposium on Biocomputing '96",
  year =         "1996",
  editor =       "Lawrence Hunter and Teri E. Klein",
  pages =        "500--511",
  publisher_address = "Singapore",
  publisher =    "World Scientific",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=",
  URL =          "http://www.genetic-programming.com/jkpdf/psb1996.pdf",
  abstract =     "This paper describes how the biological theory of gene
                 duplication described in Susumu Ohno's provocative
                 book, Evolution by Means of Gene Duplication, was
                 brought to bear on a vexatious problem from the domain
                 of automated machine learning, namely the problem of
                 architecture discovery. An automatic programming system
                 should require that the user make as few
                 problem-specific decisions as possible concerning the
                 size, shape, and character of the ultimate solution to
                 the problem. Six new architecture-altering operations
                 enable genetic programming to automatically discover an
                 appropriate architecture for solving the problem
                 concurrently with its efforts to solve the problem.
                 These architecture-altering operations were motivated
                 by the way that new biological structures, functions,
                 and behaviors arise in nature using gene duplication.
                 Genetic programming with the new architecture-altering
                 operations was then applied to the transmembrane
                 protein segment identification problem. The
                 out-of-sample error rate for the best
                 genetically-evolved program achieved was slightly
                 better than that of previously-reported human-written
                 algorithms for this problem.",
  notes =        "PSB 96",

Genetic Programming entries for John Koza David Andre