On the Search Space of Genetic Programming and Its Relation to Nature's Search Space

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Marc Ebner",
  title =        "On the Search Space of Genetic Programming and Its
                 Relation to Nature's Search Space",
  booktitle =    "Proceedings of the Congress on Evolutionary
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and 
                 Marc Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "1357--1361",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, models of
                 evolutionary computation, fixed size bit strings,
                 identity function, introns, nature, neutral mutations,
                 phenotypical behaviour, quantitative analysis, search
                 space, sequence space, variable length structures,
                 combinatorial mathematics, reachability analysis,
                 search problems",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=",
  URL =          "http://stubber.math-inf.uni-greifswald.de/~ebner/resources/uniTu/gpfitness.pdf",
  DOI =          "doi:10.1109/CEC.1999.782609",
  abstract =     "The size of the search space has been analysed for
                 genetic programming and genetic algorithms. It is
                 highly unlikely to find any single individual in this
                 huge search space. However, genetic programming with
                 variable length structures differs from standard
                 genetic algorithms where fixed size bit strings are
                 used in that usually many different individuals show
                 the same pheno-typical behaviour due to introns.
                 Therefore, finding any given behaviour is not as
                 difficult as the size of the search space suggests. A
                 quantitative analysis is presented for the number of
                 individuals that code for the identity function. The
                 identity function is important in the analysis of the
                 search space because it can be used to construct
                 individuals showing the same behavior as any given
                 individual. Finally, an analogy is drawn to nature's
                 sequence space which suggests possible directions for
                 future research. The representation should be chosen
                 such that all possible behaviours are reachable within
                 a comparatively small number of steps from any given
                 behaviour and the individuals coding for any given
                 behaviour should be distributed randomly in the search
                 space. In addition, long paths of neutral mutations
                 should lead to individuals which code for the same
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",

Genetic Programming entries for Marc Ebner