Structural difficulty in estimation of distribution genetic programming

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

@InProceedings{Kim:2011:GECCO,
  author =       "Kangil Kim and Min Hyeok Kim and Bob McKay",
  title =        "Structural difficulty in estimation of distribution
                 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 =        "1459--1466",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001772",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Estimation of Distribution Algorithms were introduced
                 into Genetic Programming over 15 years ago, and have
                 demonstrated good performance on a range of problems,
                 but there has been little research into their
                 limitations. We apply two such algorithms - scalar and
                 vectorial Stochastic Grammar GP - to Daida's well-known
                 Lid problem, to better understand their ability to
                 learn specific structures. The scalar algorithm
                 performs poorly, but the vectorial version shows good
                 overall performance. We then extended Daida's problem
                 to explore the vectorial algorithm's ability to find
                 even more specific structures, finding that the
                 performance fell off rapidly as the specificity of the
                 required structure increased. Thus although this
                 particular system has less severe structural difficulty
                 issues than standard GP, it is by no means free of
                 them. Track: Genetic Programming",
  notes =        "Also known as \cite{2001772} 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 Kangil Kim MinHyeok Kim R I (Bob) McKay

Citations