Enzyme Genetic Programming

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

@InProceedings{LonTyr01,
  author =       "Michael A. Lones and Andy M. Tyrrell",
  title =        "Enzyme Genetic Programming",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation, CEC 2001",
  year =         "2001",
  pages =        "1183--1190",
  month =        "27--30 " # may,
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, biomimetic
                 representations, Metabolic Pathways, Evolutionary
                 Electronics",
  ISBN =         "0-7803-6657-3",
  URL =          "http://folk.ntnu.no/lones/lones-cec2001.pdf",
  DOI =          "doi:10.1109/CEC.2001.934325",
  abstract =     "The work reported in this paper follows from the
                 hypothesis that better performance in artificial
                 evolution can be achieved by adhering more closely to
                 the features that make natural evolution effective
                 within biological systems. An important issue in
                 evolutionary computation is the choice of solution
                 representation. Genetic programming, whilst borrowing
                 from biology in the evolutionary axis of behaviour,
                 remains firmly rooted in the artificial domain with its
                 use of a parse tree representation. Following concerns
                 that this approach does not encourage solution
                 evolvability, this paper presents an alternative method
                 modelled upon representations used by biology. Early
                 results are encouraging; demonstrating that the method
                 is competitive when applied to problems in the area of
                 combinatorial circuit design. Whilst too early to gauge
                 its suitability to a more general domain of
                 programming, these results do indicate that the concept
                 of bringing ideas from biological representations to
                 genetic programming is a promising one.",
  size =         "8 pages",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =",
}

Genetic Programming entries for Michael A Lones Andrew M Tyrrell

Citations