Bloat control in genetic programming with a histogram-based accept-reject method

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{Gardner:2011:GECCOcomp,
  author =       "Marc-Andre Gardner and Christian Gagne and 
                 Marc Parizeau",
  title =        "Bloat control in genetic programming with a
                 histogram-based accept-reject method",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 companion 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 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming: Poster",
  pages =        "187--188",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  doi =          "doi:10.1145/2001858.2001963",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  size =         "2 pages",
  abstract =     "Recent bloat control methods such as dynamic depth
                 limit (DynLimit) and Dynamic Operator Equalisation
                 (DynOpEq) aim at modifying the tree size distribution
                 in a population of genetic programs. Although they are
                 quite efficient for that purpose, these techniques have
                 the disadvantage of evaluating the fitness of many
                 bloated Genetic Programming (GP) trees, and then
                 rejecting most of them, leading to an important waste
                 of computational resources. We are proposing a method
                 that makes a histogram-based model of current GP tree
                 size distribution, and uses the so-called accept-reject
                 method for generating a population with the desired
                 target size distribution, in order to make a stochastic
                 control of bloat in the course of the evolution.
                 Experimental results show that the method is able to
                 control bloat as well as other state-of-the-art
                 methods, with minimal additional computational efforts
                 compared to standard tree-based GP.",
  notes =        "symbolic regression, Santa Fe Ant, 6 parity. Like
                 operator equalisation?? but does not need to evaluate
                 fitness before deciding if child fits into desired
                 distribution of program sizes. Cut off wrong word.
                 Above target allow size histogram falls exponentially.
                 Does not seem to limit small programs. Seem to be
                 missing point about distribution of sizes actually
                 generated by crossover. HARM-GP
                 deap.googlecode.com

                 Also known as \cite{2001963} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for Marc-Andre Gardner Christian Gagne Marc Parizeau