Discovery scientific laws by hybrid evolutionary model

被引：2

作者：

Tang, Fei ^{[1
]}

Chen, Sanfeng ^{[1
]}

Tan, Xu ^{[1
]}

Hu, Tao ^{[1
]}

Lin, Guangming ^{[1
]}

Kang, Zuo ^{[2
]}

机构：

[1] Shenzhen Inst Informat Technol, Shenzhen, Peoples R China

[2] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China

来源：

NEUROCOMPUTING | 2015年 / 148卷

基金：

中国国家自然科学基金;

关键词：

Hybrid evolutionary algorithm; Discover scientific laws; Genetic programming; CLASSIFICATION;

D O I：

10.1016/j.neucom.2012.07.058

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Constructing a mathematical model is an important issue in engineering application and scientific research. Discovery high-level knowledge such as laws of natural science in the observed data automatically is a very important and difficult task in systematic research. The authors have got some significant results with respect to this problem. In this paper, high-level knowledge modelled by systems of ordinary differential equations (ODES) is discovered in the observed data routinely by a hybrid evolutionary algorithm called HEA-GP. The application is used to demonstrate the potential of HEA-GP. The results show that the dynamic models discovered automatically in observed data by computer sometimes can compare with the models discovered by humanity. In addition, a prototype of KDD Automatic System has been developed which can be used to discover models in observed data automatically. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：143 / 149

页数：7

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