Use of infeasible individuals in probabilistic model building genetic network programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{XiannengLi:2011:GECCO,
  author =       "Xianneng Li and Shingo Mabu and Kotaro Hirasawa",
  title =        "Use of infeasible individuals in probabilistic model
                 building genetic network 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 =        "601--608",
  keywords =     "genetic algorithms, genetic programming, genetic
                 network programming, Estimation of distribution
                 algorithms",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001659",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Classical EDAs generally use truncation selection to
                 estimate the distribution of the feasible (good)
                 individuals while ignoring the infeasible (bad) ones.
                 However, various research in EAs reported that the
                 infeasible individuals may affect and help the problem
                 solving. This paper proposed a new method to use the
                 infeasible individuals by studying the sub-structures
                 rather than the entire individual structures to solve
                 Reinforcement Learning (RL) problems, which generally
                 factorise their entire solutions to the sequences of
                 state-action pairs. This work was studied in a recent
                 graph-based EDA named Probabilistic Model Building
                 Genetic Network Programming (PMBGNP) which can solve RL
                 problems successfully. The effectiveness of this work
                 is verified in a RL problem, i.e., robot control,
                 comparing with some other related work.",
  notes =        "Also known as \cite{2001659} 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 Xianneng Li Shingo Mabu Kotaro Hirasawa

Citations