Comparison Between Genetic Algorithm and Genetic Programming Approach for Modeling the Stress Distribution

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@Article{brezocnik:2005:MMP,
  author =       "Miran Brezocnik and Miha Kovacic and Leo Gusel",
  title =        "Comparison Between Genetic Algorithm and Genetic
                 Programming Approach for Modeling the Stress
                 Distribution",
  journal =      "Materials and Manufacturing Processes",
  year =         "2005",
  volume =       "20",
  number =       "3",
  pages =        "497--508",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, Metal
                 forming, Stress distribution, System modelling",
  ISSN =         "1042-6914",
  URL =          "http://journalsonline.tandf.co.uk/openurl.asp?genre=article&issn=1042-6914&volume=20&issue=3&spage=497",
  DOI =          "doi:10.1081/AMP-200053541",
  abstract =     "We compare genetic algorithm (GA) and genetic
                 programming (GP) for system modelling in metal forming.
                 As an example, the radial stress distribution in a
                 cold-formed specimen (steel X6Cr13) was predicted by GA
                 and GP. First, cylindrical workpieces were forward
                 extruded and analysed by the visioplasticity method.
                 After each extrusion, the values of independent
                 variables (radial position of measured stress node,
                 axial position of measured stress node, and coefficient
                 of friction) were collected. These variables influence
                 the value of the dependent variable, radial stress. On
                 the basis of training data, different prediction models
                 for radial stress distribution were developed
                 independently by GA and GP. The obtained models were
                 tested with the testing data. The research has shown
                 that both approaches are suitable for system modeling.
                 However, if the relations between input and output
                 variables are complex, the models developed by the GP
                 approach are much more accurate.",
  notes =        "A1 Laboratory for Intelligent Manufacturing Systems,
                 University of Maribor, Faculty of Mechanical
                 Engineering, Maribor, Slovenia

                 A2 Laboratory for Material Forming, University of
                 Maribor, Faculty of Mechanical Engineering, Maribor,
                 Slovenia",
}

Genetic Programming entries for Miran Brezocnik Miha Kovacic Leo Gusel

Citations