Neural Computing and Applications

, Volume 21, Issue 1, pp 171–187 | Cite as

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

  • Amir Hossein GandomiEmail author
  • Amir Hossein Alavi
Original Article


This paper presents a new approach for behavioral modeling 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 analyzed 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.


Data mining Structural engineering Multi-gene genetic programming Formulation 


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Department of Civil EngineeringUniversity of AkronAkronUSA
  2. 2.School of Civil Engineering, Iran University of Science and TechnologyTehranIran

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