Discovery scientific laws by hybrid evolutionary model

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  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
  journal =      "Neurocomputing",
  volume =       "148",
  pages =        "143--149",
  year =         "2015",
  ISSN =         "0925-2312",
  DOI =          "doi:10.1016/j.neucom.2012.07.058",
  URL =          "",
  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 Zuo Kang