Assessment of artificial neural network and genetic programming as predictive tools

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

@Article{Gandomi:2015:AES,
  author =       "Amir H. Gandomi and David A. Roke",
  title =        "Assessment of artificial neural network and genetic
                 programming as predictive tools",
  journal =      "Advances in Engineering Software",
  year =         "2015",
  volume =       "88",
  pages =        "63--72",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, Artificial neural networks,
                 Over-fitting, Explicit formulation, Punching shear, RC
                 slabs, Parametric study",
  ISSN =         "0965-9978",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0965997815000861",
  DOI =          "doi:10.1016/j.advengsoft.2015.05.007",
  abstract =     "Soft computing techniques have been widely used during
                 the last two decades for nonlinear system modeling,
                 specifically as predictive tools. In this study, the
                 performances of two well-known soft computing
                 predictive techniques, artificial neural network (ANN)
                 and genetic programming (GP), are evaluated based on
                 several criteria, including over-fitting potential. A
                 case study in punching shear prediction of RC slabs is
                 modelled here using a hybrid ANN (which includes
                 simulated annealing and multi-layer perception) and an
                 established GP variant called gene expression
                 programming. The ANN and GP results are compared to
                 values determined from several design codes. For more
                 verification, external validation and parametric
                 studies were also conducted. The results of this study
                 indicate that model acceptance criteria should include
                 engineering analysis from parametric studies.",
}

Genetic Programming entries for A H Gandomi David Roke

Citations