Automatic discovery of scientific laws in observed data by asynchronous parallel evolutionary algorithm

Author

Li, Yan ; Kang, Zhuo ; Kang, Lishan ; Cao, Hongqing ; Liu, Pu

Author_Institution

State Key Lab. of Software Eng., Wuhan Univ., China

fYear

2001

fDate

2001

Firstpage

180

Lastpage

184

Abstract

How to discover high-level knowledge such as laws of natural science in observed data automatically is a very important and difficult task in scientific research. High level knowledge modeled by ordinary differential equations (ODES) is discovered in observed dynamic data automatically by an asynchronous parallel evolutionary algorithm called AP-HEMA. A numerical example is used to demonstrate the potential of AP-HEMA. The results show that the dynamic models discovered automatically in the observed dynamic data by computer can sometimes compare with models discovered by humans

Keywords

data mining; differential equations; evolutionary computation; natural sciences computing; parallel algorithms; AP-HEMA; ODES; asynchronous parallel evolutionary algorithm; automatic scientific law discovery; high-level knowledge discovery; natural science laws; observed data; observed dynamic data; ordinary differential equations; scientific research; Concurrent computing; Data mining; Differential equations; Evolutionary computation; Humans; Impedance; Laboratories; Parallel algorithms; Predictive models; Software engineering;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Multimedia Applications, 2001. ICCIMA 2001. Proceedings. Fourth International Conference on

Conference_Location

Yokusika City

Print_ISBN

0-7695-1312-3

Type

conf

DOI

10.1109/ICCIMA.2001.970464

Filename

970464