### 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 language | English |
---|---|

Title of host publication | Information Search, Integration, and Personalization - 12th International Workshop, ISIP 2018, Revised Selected Papers |

Editors | Dimitris Kotzinos, Dominique Laurent, Nicolas Spyratos, Yuzuru Tanaka, Rin-ichiro Taniguchi |

Publisher | Springer Verlag |

Pages | 89-103 |

Number of pages | 15 |

ISBN (Print) | 9783030302832 |

DOIs | |

Publication status | Published - Jan 1 2019 |

Event | 12th International Workshop on Information Search, Integration and Personalization, ISIP 2018 - Fukuoka, Japan Duration: May 14 2018 → May 15 2018 |

### Publication series

Name | Communications in Computer and Information Science |
---|---|

Volume | 1040 |

ISSN (Print) | 1865-0929 |

ISSN (Electronic) | 1865-0937 |

### Conference

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

Country | Japan |

City | Fukuoka |

Period | 5/14/18 → 5/15/18 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Computer Science(all)
- Mathematics(all)

### Cite this

*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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*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

}

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

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

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

ER -