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International Conference on Neural Information Processing

ICONIP 2017: Neural Information Processing pp 356–365Cite as

Application of Grammatical Swarm to Symbolic Regression Problem

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10637))

Abstract

Grammatical Swarm (GS), which is one of the evolutionary computations, is designed to find the function, the program or the program segment satisfying the design objective. Since the candidate solutions are defined as the bit-strings, the use of the translation rules translates the bit-strings into the function or the program. The swarm of particles is evolved according to Particle Swarm Optimization (PSO) in order to find the better solution. The aim of this study is to improve the convergence property of GS by changing the traditional PSO in GS with the other PSOs such as Particle Swarm Optimization with constriction factor, Union of Global and Local Particle Swarm Optimizations, Comprehensive Learning Particle Swarm Optimization, Particle Swarm Optimization with Second Global best Particle and Particle Swarm Optimization with Second Personal best Particle. The improved GS algorithms, therefore, are named as Grammatical Swarm with constriction factor (GS-cf), Union of Global and Local Grammatical Swarm (UGS), Comprehensive Learning Grammatical Swarm (CLGS), Grammatical Swarm with Second Global best Particle (SG-GS) and Grammatical Swarm with Second Personal best Particle (SG-GS), respectively. Symbolic regression problem is considered as the numerical example. The original GS is compared with the other algorithms. The effect of the model parameters for the convergence properties of the algorithms are discussed in the preliminary experiments. Then, except for CLGS and UGS, the convergence speeds of the other algorithms are faster than that of the original GS. Especially, the convergence properties of GS-cf and SP-GS are fastest among them.

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Author information

Authors and Affiliations

  1. Graduate School of Informatics, Nagoya University, Nagoya, Japan

    Eisuke Kita

  2. Graduate School of Information Sciences, Nagoya University, Nagoya, Japan

    Risako Yamamoto & Hideyuki Sugiura

  3. Institute of Innovation for Future Society, Nagoya University, Nagoya, Japan

    Yi Zuo

Authors
  1. Eisuke Kita
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  2. Risako Yamamoto
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  3. Hideyuki Sugiura
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  4. Yi Zuo
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Corresponding author

Correspondence to Eisuke Kita .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Kita, E., Yamamoto, R., Sugiura, H., Zuo, Y. (2017). Application of Grammatical Swarm to Symbolic Regression Problem. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_37

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  • DOI: https://doi.org/10.1007/978-3-319-70093-9_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70092-2

  • Online ISBN: 978-3-319-70093-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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