Grammar-based genetic programming with dependence learning and bayesian network classifier

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Pak-Kan Wong and Leung-Yau Lo and Man-Leung Wong and 
                 Kwong-Sak Leung",
  title =        "Grammar-based genetic programming with dependence
                 learning and bayesian network classifier",
  booktitle =    "GECCO '14: Proceedings of the 2014 conference on
                 Genetic and evolutionary computation",
  year =         "2014",
  editor =       "Christian Igel and Dirk V. Arnold and 
                 Christian Gagne and Elena Popovici and Anne Auger and 
                 Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and 
                 Kalyanmoy Deb and Benjamin Doerr and James Foster and 
                 Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and 
                 Hitoshi Iba and Christian Jacob and Thomas Jansen and 
                 Yaochu Jin and Marouane Kessentini and 
                 Joshua D. Knowles and William B. Langdon and Pedro Larranaga and 
                 Sean Luke and Gabriel Luque and John A. W. McCall and 
                 Marco A. {Montes de Oca} and Alison Motsinger-Reif and 
                 Yew Soon Ong and Michael Palmer and 
                 Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and 
                 Guenther Ruhe and Tom Schaul and Thomas Schmickl and 
                 Bernhard Sendhoff and Kenneth O. Stanley and 
                 Thomas Stuetzle and Dirk Thierens and Julian Togelius and 
                 Carsten Witt and Christine Zarges",
  isbn13 =       "978-1-4503-2662-9",
  pages =        "959--966",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "",
  DOI =          "doi:10.1145/2576768.2598256",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Grammar-Based Genetic Programming formalises
                 constraints on the solution structure based on domain
                 knowledge to reduce the search space and generate
                 grammatically correct individuals. Nevertheless,
                 building blocks in a program can often be dependent, so
                 the effective search space can be further reduced.
                 Approaches have been proposed to learn the dependence
                 using probabilistic models and shown to be useful in
                 finding the optimal solutions with complex structure.
                 It raises questions on how to use the individuals in
                 the population to uncover the underlying dependence.
                 Usually, only the good individuals are selected. To
                 model the dependence better, we introduce Grammar-Based
                 Genetic Programming with Bayesian Network Classifier
                 (GBGPBC) which also uses poorer individuals. With the
                 introduction of class labels, we further propose a
                 refinement technique on probability distribution based
                 on class label. Our results show that GBGPBC performs
                 well on two benchmark problems. These techniques boost
                 the performance of our system.",
  notes =        "Also known as \cite{2598256} GECCO-2014 A joint
                 meeting of the twenty third international conference on
                 genetic algorithms (ICGA-2014) and the nineteenth
                 annual genetic programming conference (GP-2014)",

Genetic Programming entries for Pak-Kan Wong "Peter" Leung-Yau Lo Man Leung Wong Kwong-Sak Leung