Effective pre-processing of genetic programming for solving symbolic regression in equation extraction

Issei Koga, Kenji Ono

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    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.

    Original languageEnglish
    Title of host publicationInformation Search, Integration, and Personalization - 12th International Workshop, ISIP 2018, Revised Selected Papers
    EditorsDimitris Kotzinos, Dominique Laurent, Nicolas Spyratos, Yuzuru Tanaka, Rin-ichiro Taniguchi
    PublisherSpringer Verlag
    Pages89-103
    Number of pages15
    ISBN (Print)9783030302832
    DOIs
    Publication statusPublished - Jan 1 2019
    Event12th International Workshop on Information Search, Integration and Personalization, ISIP 2018 - Fukuoka, Japan
    Duration: May 14 2018May 15 2018

    Publication series

    NameCommunications in Computer and Information Science
    Volume1040
    ISSN (Print)1865-0929
    ISSN (Electronic)1865-0937

    Conference

    Conference12th International Workshop on Information Search, Integration and Personalization, ISIP 2018
    CountryJapan
    CityFukuoka
    Period5/14/185/15/18

    All Science Journal Classification (ASJC) codes

    • Computer Science(all)
    • Mathematics(all)

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