A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems
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- @Article{journals/nca/GandomiA12,
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author = "Amir Hossein Gandomi and Amir Hossein Alavi",
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title = "A new multi-gene genetic programming approach to
nonlinear system modeling. Part {I}: materials and
structural engineering problems",
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journal = "Neural Computing and Applications",
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year = "2012",
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volume = "21",
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number = "1",
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pages = "171--187",
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month = feb,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Data mining,
Structural engineering, Multi-gene genetic programming,
Formulation",
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ISSN = "0941-0643",
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DOI = "doi:10.1007/s00521-011-0734-z",
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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.",
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notes = "See \cite{journals/nca/GandomiA12a}",
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affiliation = "Department of Civil Engineering, University of Akron,
Akron, OH 44325-3905, USA",
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bibdate = "2012-01-17",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/nca/nca21.html#GandomiA12",
- }
Genetic Programming entries for
A H Gandomi
A H Alavi
Citations