Investigating the success of spatial coevolution

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

@InProceedings{1068096,
  author =       "Nathan Williams and Melanie Mitchell",
  title =        "Investigating the success of spatial coevolution",
  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 =       "1",
  ISBN =         "1-59593-010-8",
  pages =        "523--530",
  address =      "Washington DC, USA",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p523.pdf",
  DOI =          "doi:10.1145/1068009.1068096",
  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, Coevolution,
                 resource sharing, spatial evolution",
  size =         "8 pages",
  abstract =     "We investigate the results of coevolution of spatially
                 distributed populations. In particular, we describe
                 work in which a simple function approximation problem
                 is used to compare different spatial evolutionary
                 methods. Our work shows that, on this problem, spatial
                 coevolution is dramatically more successful than any
                 other spatial evolutionary scheme we tested. Our
                 results support two hypotheses about the source of
                 spatial coevolution's superior performance: (1) spatial
                 coevolution allows population diversity to persist over
                 many generations; and (2) spatial coevolution produces
                 training examples ({"}parasites{"}) that specifically
                 target weaknesses in models ({"}hosts{"}). The precise
                 mechanisms by which the combination of spatial
                 embedding and coevolution produces these results are
                 still not well understood.",
  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",
}

Genetic Programming entries for Nathan Williams Melanie Mitchell

Citations