A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{journals/nca/GandomiA12,
  author =       "Amir Hossein Gandomi and Amir Hossein Alavi",
  title =        "A new multi-gene genetic programming approach to
                 nonlinear system modeling. Part {I}: materials and
                 structural engineering problems",
  journal =      "Neural Computing and Applications",
  year =         "2012",
  volume =       "21",
  number =       "1",
  pages =        "171--187",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0941-0643",
  DOI =          "doi:10.1007/s00521-011-0734-z",
  abstract =     "This paper presents a new approach for behavioural
                 modelling of structural engineering systems using a
                 promising variant of genetic programming (GP), namely
                 multi-gene genetic programming (MGGP). MGGP effectively
                 combines the model structure selection ability of the
                 standard GP with the parameter estimation power of
                 classical regression to capture the nonlinear
                 interactions. The capabilities of MGGP are illustrated
                 by applying it to the formulation of various complex
                 structural engineering problems. The problems analysed
                 herein include estimation of: (1) compressive strength
                 of high-performance concrete (2) ultimate pure bending
                 of steel circular tubes, (3) surface roughness in
                 end-milling, and (4) failure modes of beams subjected
                 to patch loads. The derived straightforward equations
                 are linear combinations of nonlinear transformations of
                 the predictor variables. The validity of MGGP is
                 confirmed by applying the derived models to the parts
                 of the experimental results that are not included in
                 the analyses. The MGGP-based equations can reliably be
                 employed for pre-design purposes. The results of MSGP
                 are found to be more accurate than those of solutions
                 presented in the literature. MGGP does not require
                 simplifying assumptions in developing the models.",
  notes =        "See \cite{journals/nca/GandomiA12a}",
  affiliation =  "Department of Civil Engineering, University of Akron,
                 Akron, OH 44325-3905, USA",
  bibdate =      "2012-01-17",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/nca/nca21.html#GandomiA12",
}

Genetic Programming entries for A H Gandomi A H Alavi

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