Use of infeasible individuals in probabilistic model building genetic network programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @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