### 抄録

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.

元の言語 | 英語 |
---|---|

ホスト出版物のタイトル | Information Search, Integration, and Personalization - 12th International Workshop, ISIP 2018, Revised Selected Papers |

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

出版者 | Springer Verlag |

ページ | 89-103 |

ページ数 | 15 |

ISBN（印刷物） | 9783030302832 |

DOI | |

出版物ステータス | 出版済み - 1 1 2019 |

イベント | 12th International Workshop on Information Search, Integration and Personalization, ISIP 2018 - Fukuoka, 日本 継続期間: 5 14 2018 → 5 15 2018 |

### 出版物シリーズ

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

巻 | 1040 |

ISSN（印刷物） | 1865-0929 |

ISSN（電子版） | 1865-0937 |

### 会議

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

国 | 日本 |

市 | Fukuoka |

期間 | 5/14/18 → 5/15/18 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Computer Science(all)
- Mathematics(all)

### これを引用

*Information Search, Integration, and Personalization - 12th International Workshop, ISIP 2018, Revised Selected Papers*(pp. 89-103). (Communications in Computer and Information Science; 巻数 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.

研究成果: 著書/レポートタイプへの貢献 › 会議での発言

*Information Search, Integration, and Personalization - 12th International Workshop, ISIP 2018, Revised Selected Papers.*Communications in Computer and Information Science, 巻. 1040, Springer Verlag, pp. 89-103, 12th International Workshop on Information Search, Integration and Personalization, ISIP 2018, Fukuoka, 日本, 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 -