Parsimonious genetic programming for complex process intelligent modeling: algorithm and applications

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@Article{Wei:2010:NCA,
  title =        "Parsimonious genetic programming for complex process
                 intelligent modeling: algorithm and applications",
  author =       "Xunkai Wei and Yinghong Li and Yue Feng",
  journal =      "Neural Computing and Applications",
  year =         "2010",
  number =       "2",
  volume =       "19",
  pages =        "329--335",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0941-0643",
  publisher =    "Springer London",
  DOI =          "doi:10.1007/s00521-009-0308-5",
  size =         "7 pages",
  abstract =     "A novel genetic programming (GP) algorithm called
                 parsimonious genetic programming (PGP) for complex
                 process intelligent modeling was proposed. First, the
                 method uses traditional GP to generate nonlinear
                 input-output model sets that are represented in a
                 binary tree structure according to special
                 decomposition method. Then, it applies orthogonal least
                 squares algorithm (OLS) to estimate the contribution of
                 the branches, which refers to basic function term that
                 cannot be decomposed anymore, to the accuracy of the
                 model, so as to eliminate complex redundant subtrees
                 and enhance convergence speed. Finally, it obtains
                 simple, reliable and exact linear in parameters
                 nonlinear model via GP evolution. Simulations validate
                 that the proposed method can generate more robust and
                 interpretable models, which is obvious and easy for
                 realization in real applications. For the proposed
                 algorithm, the whole modeling process is fully
                 automatic, which is a rather promising method for
                 complex process intelligent modeling.",
  bibdate =      "2011-02-22",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/nca/nca19.html#WeiLF10",
  affiliation =  "Air Force Engineering University Engineering Institute
                 Department of Aircraft and Power Engineering Shaanxi,
                 Xi'an 710038 China",
}

Genetic Programming entries for Xunkai Wei Yinghong Li Yue Feng

Citations