Rethinking multilevel selection in genetic programming

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

@InProceedings{Wu:2011:GECCO,
  author =       "Shelly Xiaonan Wu and Wolfgang Banzhaf",
  title =        "Rethinking multilevel selection in genetic
                 programming",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0557-0",
  pages =        "1403--1410",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001765",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  note =         "Best paper",
  abstract =     "This paper aims to improve the capability of genetic
                 programming to tackle the evolution of cooperation:
                 evolving multiple partial solutions that
                 collaboratively solve structurally and functionally
                 complex problems. A multilevel genetic programming
                 approach is presented based on a new computational
                 multilevel selection framework [19]. This approach
                 considers biological group selection theory to
                 encourage cooperation, and a new cooperation operator
                 to build solutions hierarchically. It extends evolution
                 from individuals to multiple group levels, leading to
                 good performance on both individuals and groups. The
                 applicability of this approach is evaluated on 7
                 multi-class classification problems with different
                 features, such as non-linearity, skewed data
                 distribution and large feature space. The results, when
                 compared to other cooperative evolutionary algorithms
                 in the literature, demonstrate that this approach
                 improves solution accuracy and consistency, and
                 simplifies solution complexity. In addition, the
                 problem is decomposed as a result of evolution without
                 human interference.",
  notes =        "Also known as \cite{2001765} GECCO-2011 A joint
                 meeting of the twentieth international conference on
                 genetic algorithms (ICGA-2011) and the sixteenth annual
                 genetic programming conference (GP-2011)",
}

Genetic Programming entries for Shelly Xiaonan Wu Wolfgang Banzhaf

Citations