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

Fingerprint

Symbolic Regression
Genetic programming
Genetic Programming
Preprocessing
Processing
Explosion
Explosions
Eliminate
Classify
Neural Networks
Eigenvalue
Experiment
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

Cite this

Koga, I., & Ono, K. (2019). Effective pre-processing of genetic programming for solving symbolic regression in equation extraction. In D. Kotzinos, D. Laurent, N. Spyratos, Y. Tanaka, & R. Taniguchi (Eds.), Information Search, Integration, and Personalization - 12th International Workshop, ISIP 2018, Revised Selected Papers (pp. 89-103). (Communications in Computer and Information Science; Vol. 1040). Springer Verlag. https://doi.org/10.1007/978-3-030-30284-9_6

Effective pre-processing of genetic programming for solving symbolic regression in equation extraction. / Koga, Issei; Ono, Kenji.

Information Search, Integration, and Personalization - 12th International Workshop, ISIP 2018, Revised Selected Papers. ed. / Dimitris Kotzinos; Dominique Laurent; Nicolas Spyratos; Yuzuru Tanaka; Rin-ichiro Taniguchi. Springer Verlag, 2019. p. 89-103 (Communications in Computer and Information Science; Vol. 1040).

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

Koga, I & Ono, K 2019, Effective pre-processing of genetic programming for solving symbolic regression in equation extraction. in D Kotzinos, D Laurent, N Spyratos, Y Tanaka & R Taniguchi (eds), Information Search, Integration, and Personalization - 12th International Workshop, ISIP 2018, Revised Selected Papers. Communications in Computer and Information Science, vol. 1040, Springer Verlag, pp. 89-103, 12th International Workshop on Information Search, Integration and Personalization, ISIP 2018, Fukuoka, Japan, 5/14/18. https://doi.org/10.1007/978-3-030-30284-9_6
Koga I, Ono K. Effective pre-processing of genetic programming for solving symbolic regression in equation extraction. In Kotzinos D, Laurent D, Spyratos N, Tanaka Y, Taniguchi R, editors, Information Search, Integration, and Personalization - 12th International Workshop, ISIP 2018, Revised Selected Papers. Springer Verlag. 2019. p. 89-103. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-030-30284-9_6
Koga, Issei ; Ono, Kenji. / Effective pre-processing of genetic programming for solving symbolic regression in equation extraction. Information Search, Integration, and Personalization - 12th International Workshop, ISIP 2018, Revised Selected Papers. editor / Dimitris Kotzinos ; Dominique Laurent ; Nicolas Spyratos ; Yuzuru Tanaka ; Rin-ichiro Taniguchi. Springer Verlag, 2019. pp. 89-103 (Communications in Computer and Information Science).
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