Title :
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
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;
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
DOI :
10.1109/ICCIMA.2001.970464
