abstract

Extended rule-based genetic network programming

Authors:

Xianneng Li,

Kotaro HirasawaAuthors Info & Claims

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

Pages 155 - 156

https://doi.org/10.1145/2464576.2464655

Published: 06 July 2013 Publication History

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Abstract

Recent advances in rule-based systems, i.e., Learning Classifier Systems (LCSs), have shown their sequential decision-making ability with a generalization property. In this paper, a novel LCS named eXtended rule-based Genetic Network Programming (XrGNP) is proposed. Different from most of the current LCSs, the rules are represented and discovered through a graph-based evolutionary algorithm GNP, which consequently has the distinct expression ability to model and evolve the "if-then" decision-making rules. Experiments on a benchmark multi-step problem (so-called Reinforcement Learning problem) demonstrate its effectiveness.

References

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K. Hirasawa, M. Okubo, H. Katagiri, J. Hu, and J. Murata. Comparison between genetic network programming (GNP) and genetic programming (GP). In Proc. of the IEEE Congress on Evol. Comput., pages 1276--1282, 2001.

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[3]

X. Li, S. Mabu, and K. Hirasawa. A novel graph-based estimation of distribution algorithm and its extension using reinforcement learning. IEEE Trans. Evol. Comput., 2013. (early access).

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X. Li, B. Li, S. Mabu, and K. Hirasawa. A novel estimation of distribution algorithm using graph-based chromosome representation and reinforcement learning. In Proc. of the IEEE Congress on Evol. Comput., CEC '11, pages 37--44, 2011.

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X. Li, S. Mabu, and K. Hirasawa. Use of infeasible individuals in probabilistic model building genetic network programming. In Proc. of the Conf. on Genetic and Evol. Comput., GECCO '11, pages 601--608, 2011.

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Cited By

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Roshanzamir MPalhang MMirzaei A(2021)Efficiency improvement of genetic network programming by tasks decomposition in different types of environmentsGenetic Programming and Evolvable Machines10.1007/s10710-021-09402-y22:2(229-266)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s10710-021-09402-y
Roshanzamir MPalhang MMirzaei A(2019)Tasks Decomposition for Improvement of Genetic Network Programming2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)10.1109/ICCKE48569.2019.8964971(201-206)Online publication date: Oct-2019
https://doi.org/10.1109/ICCKE48569.2019.8964971
Li XHe WHirasawa K(2014)Creating stock trading rules using graph-based estimation of distribution algorithm2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900421(731-738)Online publication date: Jul-2014
https://doi.org/10.1109/CEC.2014.6900421
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Index Terms

Extended rule-based genetic network programming
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence

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Published In

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

July 2013

1798 pages

ISBN:9781450319645

DOI:10.1145/2464576

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2013

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GECCO '13

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SIGEVO

GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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

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Cited By

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Roshanzamir MPalhang MMirzaei A(2021)Efficiency improvement of genetic network programming by tasks decomposition in different types of environmentsGenetic Programming and Evolvable Machines10.1007/s10710-021-09402-y22:2(229-266)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s10710-021-09402-y
Roshanzamir MPalhang MMirzaei A(2019)Tasks Decomposition for Improvement of Genetic Network Programming2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)10.1109/ICCKE48569.2019.8964971(201-206)Online publication date: Oct-2019
https://doi.org/10.1109/ICCKE48569.2019.8964971
Li XHe WHirasawa K(2014)Creating stock trading rules using graph-based estimation of distribution algorithm2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900421(731-738)Online publication date: Jul-2014
https://doi.org/10.1109/CEC.2014.6900421
Li XHe WHirasawa K(2014)Learning and evolution of genetic network programming with knowledge transfer2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900315(798-805)Online publication date: Jul-2014
https://doi.org/10.1109/CEC.2014.6900315
Li XHirasawa K(2013)A Learning Classifier System Based on Genetic Network ProgrammingProceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics10.1109/SMC.2013.229(1323-1328)Online publication date: 13-Oct-2013
https://dl.acm.org/doi/10.1109/SMC.2013.229

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