Discovery scientific laws by hybrid evolutionary model
Created by W.Langdon from
gp-bibliography.bib Revision:1.8129
- @Article{Tang:2015:Neurocomputing,
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author = "Fei Tang and Sanfeng Chen and Xu Tan and Tao Hu and
Guangming Lin and Zuo Kang",
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title = "Discovery scientific laws by hybrid evolutionary
model",
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journal = "Neurocomputing",
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volume = "148",
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pages = "143--149",
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year = "2015",
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ISSN = "0925-2312",
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DOI = "doi:10.1016/j.neucom.2012.07.058",
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URL = "http://www.sciencedirect.com/science/article/pii/S0925231214009199",
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abstract = "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.",
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keywords = "genetic algorithms, genetic programming, Hybrid
evolutionary algorithm, Discover scientific laws",
- }
Genetic Programming entries for
Fei Tang
Sanfeng Chen
Xu Tan
Tao Hu
Guangming Lin
Wei Gao
Citations