TY - GEN

T1 - Effective pre-processing of genetic programming for solving symbolic regression in equation extraction

AU - Koga, Issei

AU - Ono, Kenji

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85072858902&partnerID=8YFLogxK

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U2 - 10.1007/978-3-030-30284-9_6

DO - 10.1007/978-3-030-30284-9_6

M3 - Conference contribution

AN - SCOPUS:85072858902

SN - 9783030302832

T3 - Communications in Computer and Information Science

SP - 89

EP - 103

BT - Information Search, Integration, and Personalization - 12th International Workshop, ISIP 2018, Revised Selected Papers

A2 - Kotzinos, Dimitris

A2 - Laurent, Dominique

A2 - Spyratos, Nicolas

A2 - Tanaka, Yuzuru

A2 - Taniguchi, Rin-ichiro

PB - Springer Verlag

T2 - 12th International Workshop on Information Search, Integration and Personalization, ISIP 2018

Y2 - 14 May 2018 through 15 May 2018

ER -