research-article

Discrete dynamical genetic programming in XCS

Authors:

Richard Preen and

Larry BullAuthors Info & Claims

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

July 2009

Pages 1299 - 1306

https://doi.org/10.1145/1569901.1570075

Published: 08 July 2009 Publication History

Abstract

A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using a discrete dynamical system representation within the XCS Learning Classifier System. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such discrete dynamical systems within XCS to solve a number of well-known test problems.

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Cited By

K Babu SSchneider Tvon Mammen SSilva SPaquete L(2023)User-centred Design and Development of a Graphical User Interface for Learning Classifier SystemsProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596357(1830-1837)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596357
Iqbal MBrowne WZhang M(2017)Extending xcs with cyclic graphs for scalability on complex boolean problemsEvolutionary Computation10.1162/EVCO_a_0016725:2(173-204)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1162/EVCO_a_00167
Chen GDouch CZhang M(2015)Using Learning Classifier Systems to Learn Stochastic Decision PoliciesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2015.241546419:6(885-902)Online publication date: Dec-2015
https://doi.org/10.1109/TEVC.2015.2415464
Show More Cited By

Index Terms

Discrete dynamical genetic programming in XCS
1. Computing methodologies
  1. Machine learning
    1. Learning settings

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cover image ACM Conferences

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

July 2009

2036 pages

ISBN:9781605583259

DOI:10.1145/1569901

General Chair:
Franz Rothlauf
University of Mainz, Germany

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 July 2009

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

K Babu SSchneider Tvon Mammen SSilva SPaquete L(2023)User-centred Design and Development of a Graphical User Interface for Learning Classifier SystemsProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596357(1830-1837)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596357
Iqbal MBrowne WZhang M(2017)Extending xcs with cyclic graphs for scalability on complex boolean problemsEvolutionary Computation10.1162/EVCO_a_0016725:2(173-204)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1162/EVCO_a_00167
Chen GDouch CZhang M(2015)Using Learning Classifier Systems to Learn Stochastic Decision PoliciesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2015.241546419:6(885-902)Online publication date: Dec-2015
https://doi.org/10.1109/TEVC.2015.2415464
Zang ZLi DWang J(2015)Learning classifier systems with memory condition to solve non-Markov problemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-014-1357-y19:6(1679-1699)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1007/s00500-014-1357-y
Preen RBull L(2013)Dynamical genetic programming in xcsfEvolutionary Computation10.1162/EVCO_a_0008021:3(361-387)Online publication date: 1-Sep-2013
https://dl.acm.org/doi/10.1162/EVCO_a_00080
Bull LAdamatzky A(2013)Evolving gene regulatory networks with mobile DNA mechanisms2013 13th UK Workshop on Computational Intelligence (UKCI)10.1109/UKCI.2013.6651280(1-7)Online publication date: Sep-2013
https://doi.org/10.1109/UKCI.2013.6651280
Bull L(2013)Imitation Programming Unorganised MachinesArtificial Intelligence, Evolutionary Computing and Metaheuristics10.1007/978-3-642-29694-9_4(63-81)Online publication date: 2013
https://doi.org/10.1007/978-3-642-29694-9_4
Bull L(2012)Evolving Boolean Networks on Tunable Fitness LandscapesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2011.217357816:6(817-828)Online publication date: 1-Dec-2012
https://dl.acm.org/doi/10.1109/TEVC.2011.2173578
Bull L(2012)Production system rules as protein complexes from genetic regulatory networks: an initial studyEvolutionary Intelligence10.1007/s12065-012-0078-35:2(59-67)Online publication date: 22-May-2012
https://doi.org/10.1007/s12065-012-0078-3
Preen RBull LLanzi P(2011)Fuzzy dynamical genetic programming in XCSFProceedings of the 13th annual conference companion on Genetic and evolutionary computation10.1145/2001858.2001952(167-168)Online publication date: 12-Jul-2011
https://dl.acm.org/doi/10.1145/2001858.2001952
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