Evolution of Both the Architecture and the Sequence of Work-Performing Steps of a Computer Program Using Genetic Programming with Architecture-Altering Operations

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

@InProceedings{koza:1995:earch,
  author =       "John R. Koza and David Andre",
  title =        "Evolution of Both the Architecture and the Sequence of
                 Work-Performing Steps of a Computer Program Using
                 Genetic Programming with Architecture-Altering
                 Operations",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "50--60",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/jkpdf/aaai1995fallsymaatm.pdf",
  URL =          "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-007.pdf",
  URL =          "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php",
  size =         "11 pages",
  abstract =     "The goal of automatic programming is to create, in an
                 automated way, a computer program that enables a
                 computer to solve a problem. Ideally, an automatic
                 programming system should require that the user
                 pre-specify as little as possible about the problem
                 environment. In particular, it is desirable that the
                 user not be required to prespecify the architecture of
                 the ultimate solution to his problem.

                 The question of how to automatically create the
                 architecture of the overall program in an evolutionary
                 approach to automatic programming, such as genetic
                 programming, has a parallel in the biological world:
                 how new structures and behaviors are created in living
                 things. This corresponds to the question of how new DNA
                 that encodes for a new protein is created in more
                 complex organisms.

                 This chapter 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 the problem of architecture
                 discovery in genetic programming. The resulting
                 biologically-motivated approach uses six new
                 architecture-altering operations to enable genetic
                 programming to automatically discover the architecture
                 of the solution at the same time as genetic programming
                 is evolving a solution to the problem.

                 Genetic programming with the architecture-altering
                 operations is used to evolve a computer program to
                 classify a given protein segment as being a
                 transmembrane domain or non-transmembrane area of the
                 protein (without biochemical knowledge, such as the
                 hydrophobicity values used in human-written algorithms
                 for this task). The best genetically-evolved program
                 achieved an out-of-sample error rate that was better
                 than that reported for other previously reported
                 human-written algorithms. This is an instance of an
                 automated machine learning algorithm matching human
                 performance on a non-trivial problem.",
  notes =        "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp} {\em
                 Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em
                 email} info@aaai.org {\em URL:} http://www.aaai.org/
                 Transmembrane protien classification 'out of sample
                 error rate that was better than that previously
                 reported for other previously repored human-written
                 algorithms' [p50]",
}

Genetic Programming entries for John Koza David Andre

Citations