Application of genetic programming for modelling of material characteristics

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@Article{Gusel201115014,
  author =       "Leo Gusel and Miran Brezocnik",
  title =        "Application of genetic programming for modelling of
                 material characteristics",
  journal =      "Expert Systems with Applications",
  volume =       "38",
  number =       "12",
  pages =        "15014--15019",
  year =         "2011",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2011.05.045",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957417411008293",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computation, Modelling, Metal forming, Material
                 characteristics",
  abstract =     "Genetic programming, which is one of the most general
                 evolutionary computation methods, was used in this
                 paper for the modelling of tensile strength and
                 electrical conductivity in cold formed material. No
                 assumptions about the form and size of expressions were
                 made in advance, but they were left to the self
                 organisation and intelligence of evolutionary process.
                 Genetic programming does this by genetically breeding a
                 population of computer programs using the principles of
                 Darwinian's natural selection and biologically inspired
                 operations. In our research, copper alloy was cold
                 formed by drawing using different process parameters
                 and then tensile strengths and electrical conductivity
                 (dependent variables) of the specimens were determined.
                 The values of independent variables (effective strain,
                 coefficient of friction) influence the value of the
                 dependent variables. Many different genetic models for
                 both dependent variables were developed by genetic
                 programming. The accuracies of the best models were
                 proved by a testing data set. Also, comparison between
                 the genetic and regression models is presented in the
                 paper. The research showed that very accurate genetic
                 models can be obtained by the proposed method.",
}

Genetic Programming entries for Leo Gusel Miran Brezocnik

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