Modeling of impact toughness of cold formed material by genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Gusel:2006:CMS,
  author =       "Leo Gusel and Miran Brezocnik",
  title =        "Modeling of impact toughness of cold formed material
                 by genetic programming",
  journal =      "Computational Materials Science",
  year =         "2006",
  volume =       "37",
  number =       "4",
  pages =        "476--482",
  month =        oct,
  email =        "mbrezocnik@uni-mb.si",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computing, metal forming, modelling, impact toughness,
                 copper alloy",
  ISSN =         "0927-0256",
  DOI =          "doi:10.1016/j.commatsci.2005.11.007",
  abstract =     "In the paper, an approach completely different from
                 the conventional methods for determination of accurate
                 models for the change of properties of cold formed
                 material, is presented. This approach is genetic
                 programming (GP) method which is based on imitation of
                 natural evolution of living organisms. The main
                 characteristic of GP is its non-deterministic way of
                 computing. 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. First, copper alloy rods were cold drawn under
                 different conditions and then impact toughness of cold
                 drawn specimens was determined by Charpy tests. The
                 values of independent variables (effective strain,
                 coefficient of friction) influence the value of the
                 dependent variable, impact toughness. On the basis of
                 training data, different prediction models for impact
                 toughness were developed by GP. Only the best models,
                 gained by genetic programming were presented in the
                 paper. Accuracy of the best models was proved with the
                 testing data set. The comparison between deviation of
                 genetic model results and regression model results
                 concerning the experimental results has showed that
                 genetic models are more precise and more varied then
                 regression models.",
}

Genetic Programming entries for Leo Gusel Miran Brezocnik

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