Linear genetic programming for prediction of circular pile scour

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@Article{Guven2009985,

• 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 = "http://www.sciencedirect.com/science/article/B6V4F-4WCTX10-3/2/805df81deb25d8c99465f876a03fc1e5",
• 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 plots.",

}

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

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