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Effective Pre-processing of Genetic Programming for Solving Symbolic Regression in Equation Extraction

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  • First Online:
Information Search, Integration, and Personalization (ISIP 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1040))

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Abstract

Estimating a form of equation that explains data is very useful to understand various physical, chemical, social, and biological phenomena. One effective approach for finding the form of an equation is to solve the symbolic regression problem using genetic programming (GP). However, this approach requires a long computation time because of the explosion of the number of combinations of candidate functions that are used as elements to construct equations. In the present paper, a novel method to effectively eliminate unnecessary functions from an initial set of functions using a deep neural network was proposed to reduce the number of computations of GP. Moreover, a method was proposed to improve the accuracy of the classification using eigenvalues when classifying whether functions are required for symbolic regression. Experiment results showed that the proposed method can successfully classify functions with over 90% of the data created in the present study.

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Author information

Authors and Affiliations

  1. Graduate School and Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, Japan

    Issei Koga

  2. Research Institute for Information Technology, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, Japan

    Kenji Ono

Authors
  1. Issei Koga
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  2. Kenji Ono
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Corresponding author

Correspondence to Kenji Ono .

Editor information

Editors and Affiliations

  1. Lab. ETIS UMR 8051, University of Paris-Seine, University of Cergy-Pontoise, ENSEA, CNRS, Cergy-Pontoise, France

    Dimitris Kotzinos

  2. Lab. ETIS UMR 8051, University of Paris-Seine, University of Cergy-Pontoise, ENSEA, CNRS, Cergy-Pontoise, France

    Dominique Laurent

  3. LRI, University of Paris-Sud, Orsay, France

    Nicolas Spyratos

  4. Hokkaido University, Sapporo, Japan

    Yuzuru Tanaka

  5. Kyushu University, Fukuoka, Japan

    Rin-ichiro Taniguchi

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Koga, I., Ono, K. (2019). Effective Pre-processing of Genetic Programming for Solving Symbolic Regression in Equation Extraction. In: Kotzinos, D., Laurent, D., Spyratos, N., Tanaka, Y., Taniguchi, Ri. (eds) Information Search, Integration, and Personalization. ISIP 2018. Communications in Computer and Information Science, vol 1040. Springer, Cham. https://doi.org/10.1007/978-3-030-30284-9_6

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  • DOI: https://doi.org/10.1007/978-3-030-30284-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30283-2

  • Online ISBN: 978-3-030-30284-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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