Exploiting disruption aversion to control code bloat

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Jason Stevens and Robert B. Heckendorn and 
                 Terry Soule",
  title =        "Exploiting disruption aversion to control code bloat",
  booktitle =    "{GECCO 2005}: Proceedings of the 2005 conference on
                 Genetic and evolutionary computation",
  year =         "2005",
  editor =       "Hans-Georg Beyer and Una-May O'Reilly and 
                 Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and 
                 Eric W. Bonabeau and Erick Cantu-Paz and 
                 Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and 
                 Edwin D. {de Jong} and Hod Lipson and Xavier Llora and 
                 Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and 
                 Terence Soule and Andy M. Tyrrell and 
                 Jean-Paul Watson and Eckart Zitzler",
  volume =       "2",
  ISBN =         "1-59593-010-8",
  pages =        "1605--1612",
  address =      "Washington DC, USA",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1605.pdf",
  DOI =          "doi:10.1145/1068009.1068281",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, 10286-1405, USA",
  month =        "25-29 " # jun,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, code bloat,
                 code growth, effective fitness, experimentation,
                 performance, theory",
  size =         "8 pages",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:",
  abstract =     "The authors employ multiple crossovers as a novel
                 natural extension to crossovers as a mixing operator.
                 They use this as a framework to explore the ideas of
                 code growth. Empirical support is given for popular
                 theories for mechanisms of code growth. Three specific
                 algorithms for multiple crossovers are compared with
                 classic methods for performance in terms of fitness and
                 genome size. The details of the performance of these
                 algorithms is examined in detail for both practical
                 value and theoretical implications. The authors
                 conclude that multiple crossovers is a practical scheme
                 for containing code growth without a significant loss
                 of fitness.",
  notes =        "GECCO-2005 A joint meeting of the fourteenth
                 international conference on genetic algorithms
                 (ICGA-2005) and the tenth annual genetic programming
                 conference (GP-2005).

                 ACM Order Number 910052

                 0-1-4 problem and parabola (9 points pop=64). hardedge,
                 softedge, multiedge",

Genetic Programming entries for Jason Stevens Robert B Heckendorn Terence Soule