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:1.8051

@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",
• month = feb,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Data mining, Structural engineering, Multi-gene genetic programming, Formulation",
• 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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