Combined neural network--genetic programming for sediment transport

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Singh:2007:ICE,
  author =       "Arvind K. Singh and M. C. Deo and V. Sanil Kumar",
  title =        "Combined neural network--genetic programming for
                 sediment transport",
  journal =      "Proceedings of the Institution of Civil Engineers",
  year =         "2007",
  volume =       "160",
  number =       "3",
  pages =        "113--119",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1741-7597",
  DOI =          "doi:10.1680/maen.2007.160.3.113",
  size =         "7 pages",
  abstract =     "The planning, operation, design and maintenance of
                 almost all harbour and coastal engineering facilities
                 call for an estimation of the longshore sediment
                 transport rate. This is currently and popularly done
                 with the help of empirical equations. In this paper an
                 alternative approach based on a combination of two soft
                 computing tools, namely neural networks and genetic
                 programming, is suggested. Such a combination was found
                 to produce better results than the individual use of
                 neural networks or genetic programming. The ability of
                 the neural network to approximate a non-linear function
                 coupled with the efficiency of the genetic programming
                 to make an optimum search over the solution domain
                 seems to result in a better prediction.",
  notes =        "Karwar. NN-GP

                 'The current study highlighted complexities and
                 uncertainties associated with the problem of prediction
                 of the rate of coastal sediment transport. It has shown
                 that the use of soft computing tools, such as NN or GP
                 can significantly improve the accuracy of prediction
                 compared with the popular regression models. In
                 addition, a further improvement in the quality of
                 prediction can be achieved by a combined NN-GP model.
                 In such a case the NN can first carry out an effective
                 function approximation and later the GP could seek the
                 correct solution through an appropriate search over the
                 solution domain.'

                 Discussed by \cite{Gandomi:2011:Discussion}

                 Department of Civil Engineering, IIT Bombay",
}

Genetic Programming entries for Arvind K Singh M C Deo V Sanil Kumar

Citations