Symbiosis, complexification and simplicity under GP

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Lichodzijewski:2010:gecco,
  author =       "Peter Lichodzijewski and Malcolm I. Heywood",
  title =        "Symbiosis, complexification and simplicity under GP",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  year =         "2010",
  editor =       "Juergen Branke and Martin Pelikan and Enrique Alba and 
                 Dirk V. Arnold and Josh Bongard and 
                 Anthony Brabazon and Juergen Branke and Martin V. Butz and 
                 Jeff Clune and Myra Cohen and Kalyanmoy Deb and 
                 Andries P Engelbrecht and Natalio Krasnogor and 
                 Julian F. Miller and Michael O'Neill and Kumara Sastry and 
                 Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and 
                 Carsten Witt",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "853--860",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830640",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Models of Genetic Programming (GP) frequently reflect
                 a neo-Darwinian view to evolution in which inheritance
                 is based on a process of gradual refinement and the
                 resulting solutions take the form of single monolithic
                 programs. Conversely, introducing an explicitly
                 symbiotic model of inheritance makes a
                 divide-and-conquer metaphor for problem decomposition
                 central to evolution. Benchmarking gradualist versus
                 symbiotic models of evolution under a common
                 evolutionary framework illustrates that not only does
                 symbiosis result in more accurate solutions, but the
                 solutions are also much simpler in terms of instruction
                 and attribute count over a wide range of classification
                 problem domains.",
  notes =        "Also known as \cite{1830640} GECCO-2010 A joint
                 meeting of the nineteenth international conference on
                 genetic algorithms (ICGA-2010) and the fifteenth annual
                 genetic programming conference (GP-2010)",
}

Genetic Programming entries for Peter Lichodzijewski Malcolm Heywood

Citations