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,
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author = "S. G. Patil and S. Mandal and A. V. Hegde",
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title = "Genetic algorithm based support vector machine
regression in predicting wave transmission of
horizontally interlaced multi-layer moored floating
pipe breakwater",
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journal = "Advances in Engineering Software",
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volume = "45",
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number = "1",
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pages = "203--212",
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year = "2012",
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ISSN = "0965-9978",
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DOI = "doi:10.1016/j.advengsoft.2011.09.026",
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URL = "http://www.sciencedirect.com/science/article/pii/S0965997811002651",
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keywords = "genetic algorithms, genetic programming, Support
vector machine, Artificial neural network, ANFIS,
Floating breakwater, HIMMFPB, Wave transmission",
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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
Sandip Mandal
Amarnath Hegde
Citations