Discovery scientific laws by hybrid evolutionary model

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@Article{Tang:2015:Neurocomputing,

• author = "Fei Tang and Sanfeng Chen and Xu Tan and Tao Hu and Guangming Lin and Zuo Kang",
• title = "Discovery scientific laws by hybrid evolutionary model",
• journal = "Neurocomputing",
• volume = "148",
• pages = "143--149",
• year = "2015",
• ISSN = "0925-2312",
• DOI = "doi:10.1016/j.neucom.2012.07.058",
• URL = "http://www.sciencedirect.com/science/article/pii/S0925231214009199",
• 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.",
• 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