Prediction of high performance concrete strength using Genetic Programming with geometric semantic genetic operators

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@Article{Castelli:2013:ESA,
  author =       "Mauro Castelli and Leonardo Vanneschi and Sara Silva",
  title =        "Prediction of high performance concrete strength using
                 Genetic Programming with geometric semantic genetic
                 operators",
  journal =      "Expert Systems with Applications",
  volume =       "40",
  number =       "17",
  pages =        "6856--6862",
  year =         "2013",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2013.06.037",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957417413004326",
  keywords =     "genetic algorithms, genetic programming, High
                 performance concrete, Strength prediction, Artificial
                 intelligence, Geometric operators, Semantics, Weka,
                 Linear regression, Square Regression, Isotonic
                 Regression, Radial Basis Function Network, RBF, SVM,
                 ANN",
  abstract =     "Concrete is a composite construction material made
                 primarily with aggregate, cement, and water. In
                 addition to the basic ingredients used in conventional
                 concrete, high-performance concrete incorporates
                 supplementary cementitious materials, such as fly ash
                 and blast furnace slag, and chemical admixture, such as
                 superplasticizer. Hence, high-performance concrete is a
                 highly complex material and modelling its behaviour
                 represents a difficult task. In this paper, we propose
                 an intelligent system based on Genetic Programming for
                 the prediction of high-performance concrete strength.
                 The system we propose is called Geometric Semantic
                 Genetic Programming, and it is based on recently
                 defined geometric semantic genetic operators for
                 Genetic Programming. Experimental results show the
                 suitability of the proposed system for the prediction
                 of concrete strength. In particular, the new method
                 provides significantly better results than the ones
                 produced by standard Genetic Programming and other
                 machine learning methods, both on training and on
                 out-of-sample data.",
  notes =        "page6861 'better (than) other well-known machine
                 learning techniques'",
}

Genetic Programming entries for Mauro Castelli Leonardo Vanneschi Sara Silva

Citations