Binary encoding for prototype tree of probabilistic model building GP

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Toshihiko Yanase and Yoshihiko Hasegawa and 
                 Hitoshi Iba",
  title =        "Binary encoding for prototype tree of probabilistic
                 model building GP",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "1147--1154",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP,",
  DOI =          "doi:10.1145/1569901.1570055",
  abstract =     "In recent years, program evolution algorithms based on
                 the estimation of distribution algorithm (EDA) have
                 been proposed to improve search ability of genetic
                 programming (GP) and to overcome GP-hard problems. One
                 such method is the probabilistic prototype tree (PPT)
                 based algorithm. The PPT based method explores the
                 optimal tree structure by using the full tree whose
                 number of child nodes is maximum among possible trees.
                 This algorithm, however, suffers from problems arising
                 from function nodes having different number of child
                 nodes. These function nodes cause intron nodes, which
                 do not affect the fitness function. Moreover, the
                 function nodes having many child nodes increase the
                 search space and the number of samples necessary for
                 properly constructing the probabilistic model. In order
                 to solve this problem, we propose binary encoding for
                 PPT. Here, we convert each function node to a subtree
                 of binary nodes where the converted tree is correct in
                 grammar. Our method reduces ineffectual search space,
                 and the binary encoded tree is able to express the same
                 tree structures as the original method. The
                 effectiveness of the proposed method is demonstrated
                 through the use of two computational experiments.",
  notes =        "GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",

Genetic Programming entries for Toshihiko Yanase Yoshihiko Hasegawa Hitoshi Iba