Linear genetic programming to scour below submerged pipeline

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

  author =       "H. Md. Azamathulla and Aytac Guven and 
                 Yusuf Kagan Demir",
  title =        "Linear genetic programming to scour below submerged
  journal =      "Ocean Engineering",
  volume =       "38",
  number =       "8-9",
  pages =        "995--1000",
  year =         "2011",
  month =        jun,
  ISSN =         "0029-8018",
  DOI =          "doi:10.1016/j.oceaneng.2011.03.005",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Local scour,
                 Neuro-fuzzy, Pipelines",
  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 below a pipeline.
                 The data sets of laboratory measurements were collected
                 from published literature and 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 were observed to be in
                 good agreement with measured data, and quite better
                 than ANFIS and regression-based equation of scour depth
                 at submerged pipeline.",

Genetic Programming entries for Hazi Mohammad Azamathulla Aytac Guven Yusuf Kagan Demir