Comparative Assessment of the Hybrid Genetic Algorithm-Artificial Neural Network and Genetic Programming Methods for the Prediction of Longitudinal Velocity Field around a Single Straight Groyne

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@Article{journals/asc/SafarzadehZB17,
  author =       "Akbar Safarzadeh and Amir Hossein Zaji and 
                 Hossein Bonakdari",
  title =        "Comparative Assessment of the Hybrid Genetic
                 Algorithm-Artificial Neural Network and Genetic
                 Programming Methods for the Prediction of Longitudinal
                 Velocity Field around a Single Straight Groyne",
  journal =      "Applied Soft Computing",
  year =         "2017",
  volume =       "60",
  pages =        "213--228",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, groyne,
                 artificial neural network, ANN, 3d flow field,
                 separation zone, experimental study",
  ISSN =         "1568-4946",
  bibdate =      "2017-11-22",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/asc/asc60.html#SafarzadehZB17",
  DOI =          "doi:10.1016/j.asoc.2017.06.048",
  abstract =     "In the present paper, three-dimensional flow fields
                 around single straight groynes with various lengths
                 have been discussed. The dataset of the flow field is
                 measured in the laboratory using Acoustic Doppler
                 Velocimeter (ADV). Then, the longitudinal velocity
                 field is modelled using a novel hybrid method of
                 Genetic Algorithm based artificial neural network (GAA)
                 that has the ability to automatically adjust the number
                 of hidden neurons. To investigate the proposed method's
                 performance, the results of GAA is measured and
                 compared with one of the most common genetic algorithm
                 based prediction method, namely genetic programming
                 (GP). It is concluded that that GAA model successfully
                 simulates the complex velocity field, and both the
                 velocity magnitudes and isovel shapes are well
                 predicted by this model. The results show that GAA with
                 RMSE of 0.1236 in test data has a significantly better
                 performance than the GP model with RMSE of 0.2342. In
                 addition, it was founded that the transverse coordinate
                 of the measuring point (Y*) is the most important input
                 variable.",
}

Genetic Programming entries for Akbar Safarzadeh Amir Hossein Zaji Hossein Bonakdari

Citations