Linear genetic programming for prediction of circular pile scour

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Aytac Guven and H. Md. Azamathulla and N. A. Zakaria",
  title =        "Linear genetic programming for prediction of circular
                 pile scour",
  journal =      "Ocean Engineering",
  volume =       "36",
  number =       "12-13",
  pages =        "985--991",
  year =         "2009",
  ISSN =         "0029-8018",
  DOI =          "doi:10.1016/j.oceaneng.2009.05.010",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Scour,
                 Neuro-fuzzy, Circular pile, Regression",
  abstract =     "Genetic programming (GP) has nowadays attracted the
                 attention of researchers in the prediction of hydraulic
                 data. This study presents linear genetic programming
                 (LGP), which is an extension to GP, as an alternative
                 tool in the prediction of scour depth around a circular
                 pile due to waves in medium dense silt and sand bed.
                 Field measurements were used to develop LGP models. The
                 proposed LGP models were compared with adaptive
                 neuro-fuzzy inference system (ANFIS) model results. The
                 predictions of LGP models were observed to be in good
                 agreement with measured data, and quite better than
                 ANFIS and regression-based equation of scour depth at
                 circular piles. The results were tabulated in terms of
                 statistical error measures and illustrated via scatter

Genetic Programming entries for Aytac Guven Hazi Mohammad Azamathulla Nor Azazi Zakaria