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

    Research output: Contribution to journalArticlepeer-review

    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
    Pages (from-to)89-103
    JournalCommunications in Computer and Information Science
    Volume1040
    Publication statusPublished - 2019

    Fingerprint Dive into the research topics of 'Effective Pre-processing of Genetic Programming for Solving Symbolic Regression in Equation Extraction'. Together they form a unique fingerprint.

    Cite this