Genetic algorithm based support vector machine regression in predicting wave transmission of horizontally interlaced multi-layer moored floating pipe breakwater

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@Article{Patil2012203,
  author =       "S. G. Patil and S. Mandal and A. V. Hegde",
  title =        "Genetic algorithm based support vector machine
                 regression in predicting wave transmission of
                 horizontally interlaced multi-layer moored floating
                 pipe breakwater",
  journal =      "Advances in Engineering Software",
  volume =       "45",
  number =       "1",
  pages =        "203--212",
  year =         "2012",
  ISSN =         "0965-9978",
  DOI =          "doi:10.1016/j.advengsoft.2011.09.026",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0965997811002651",
  keywords =     "genetic algorithms, genetic programming, Support
                 vector machine, Artificial neural network, ANFIS,
                 Floating breakwater, HIMMFPB, Wave transmission",
  abstract =     "Planning and design of coastal protection works like
                 floating pipe breakwater require information about the
                 performance characteristics of the structure in
                 reducing the wave energy. Several researchers have
                 carried out analytical and numerical studies on
                 floating breakwaters in the past but failed to give a
                 simple mathematical model to predict the wave
                 transmission through floating breakwaters by
                 considering all the boundary conditions. Computational
                 intelligence techniques, such as, Artificial Neural
                 Networks (ANN), fuzzy logic, genetic programming and
                 Support Vector Machine (SVM) are successfully used to
                 solve complex problems. In the present paper, a hybrid
                 Genetic Algorithm Tuned Support Vector Machine
                 Regression (GA-SVMR) model is developed to predict wave
                 transmission of horizontally interlaced multilayer
                 moored floating pipe breakwater (HIMMFPB). Furthermore,
                 optimal SVM and kernel parameters of GA-SVMR models are
                 determined by genetic algorithm. The GA-SVMR model is
                 trained on the data set obtained from experimental wave
                 transmission of HIMMFPB using regular wave flume at
                 Marine Structure Laboratory, National Institute of
                 Technology, Karnataka, Surathkal, Mangalore, India. The
                 results are compared with ANN and Adaptive Neuro-Fuzzy
                 Inference System (ANFIS) models in terms of correlation
                 coefficient, root mean square error and scatter index.
                 Performance of GA-SVMR is found to be reliably
                 superior. b-spline kernel function performs better than
                 other kernel functions for the given set of data.",
}

Genetic Programming entries for S G Patil S Mandal A V Hegde

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