Reassembling operator equalisation: a secret revealed

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Sara Silva",
  title =        "Reassembling operator equalisation: a secret
  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 =        "1395--1402",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001764",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The recent Crossover Bias theory has shown that bloat
                 in Genetic Programming can be caused by the
                 proliferation of small unfit individuals in the
                 population. Inspired by this theory, Operator
                 Equalisation is the most recent and successful bloat
                 control method available. In this work we revisit two
                 bloat control methods, the old Brood Recombination and
                 the newer Dynamic Limits, hypothesizing that together
                 they contain the two main ingredients that make
                 Operator Equalisation so successful. We reassemble
                 Operator Equalisation by joining these two ingredients
                 in a hybrid method, and test it in a hard real world
                 regression problem. The results are surprising.
                 Operator Equalisation and the hybrid variants exhibit
                 completely different behaviors, and an unexpected
                 feature of Operator Equalisation is revealed, one that
                 may be the true responsible for its success: a nearly
                 flat length distribution target. We support this
                 finding with additional results, and discuss its
  notes =        "Also known as \cite{2001764} 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 Sara Silva