Genetic programming and gene expression programming for flyrock assessment due to mine blasting

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@Article{Faradonbeh:2016:IJRMMS,
  author =       "Roohollah Shirani Faradonbeh and 
                 Danial Jahed Armaghani and Masoud Monjezi and Edy Tonnizam Mohamad",
  title =        "Genetic programming and gene expression programming
                 for flyrock assessment due to mine blasting",
  journal =      "International Journal of Rock Mechanics and Mining
                 Sciences",
  volume =       "88",
  pages =        "254--264",
  year =         "2016",
  ISSN =         "1365-1609",
  DOI =          "doi:10.1016/j.ijrmms.2016.07.028",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1365160916301563",
  abstract =     "This research is aimed to develop new practical
                 equations to predict flyrock distance based on genetic
                 programming (GP) and genetic expression programming
                 (GEP) techniques. For this purpose, 97 blasting
                 operations in Delkan iron mine, Iran were investigated
                 and the most effective parameters on flyrock were
                 recorded. A database comprising of five inputs (i.e.
                 burden, spacing, stemming length, hole depth, and
                 powder factor) and one output (flyrock) was prepared to
                 develop flyrock distance. Several GP and GEP models
                 were proposed to predict flyrock considering the
                 modeling procedures of them. To compare the performance
                 prediction of the developed models, coefficient of
                 determination (R2), mean absolute error (MAE), root
                 mean squared error (RMSE) and variance account for
                 (VAF) were computed and then, the best GP and GEP
                 models were selected. According to the obtained
                 results, it was found that the best flyrock predictive
                 model is the GEP based-model. As an example,
                 considering results of RMSE, values of 2.119 and 2.511
                 for training and testing datasets of GEP model,
                 respectively show higher accuracy of this model in
                 predicting flyrock, while, these values were obtained
                 as 5.788 and 10.062 for GP model.",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 expression programming, Blasting, Flyrock distance",
}

Genetic Programming entries for Roohollah Shirani Faradonbeh Danial Jahed Armaghani Masoud Monjezi Edy Tonnizam Mohamad

Citations