Genetic programming approach to determining of metal materials properties

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

@Article{Brezocnik:2002:JIM,
  author =       "Miran Brezocnik and Joze Balic and Karl Kuzman",
  title =        "Genetic programming approach to determining of metal
                 materials properties",
  journal =      "Journal of Intelligent Manufacturing",
  year =         "2002",
  volume =       "13",
  number =       "1",
  pages =        "5--17",
  month =        feb,
  email =        "joze.balic@uni-mb.si",
  keywords =     "genetic algorithms, genetic programming, materials
                 properties, metal forming, modeling,
                 self-organisation",
  ISSN =         "0956-5515",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&eissn=1572-8145&volume=13&issue=1&spage=5",
  DOI =          "doi:10.1023/A:1013693828052",
  abstract =     "The paper deals with determining metal materials
                 properties by use of genetic programming (GP). As an
                 example, the determination of the flow stress in bulk
                 forming is presented. The flow stress can be calculated
                 on the basis of known forming efficiency. The
                 experimental data obtained during pressure test serve
                 as an environment to which models for forming
                 efficiency have to be adapted during simulated
                 evolution as much as possible. By performing four
                 experiments, several different models for forming
                 efficiency are genetically developed. The models are
                 not a result of the human intelligence but of
                 intelligent evolutionary process. With regard to their
                 precision, the successful models are more or less
                 equivalent; they differ mainly in size, shape, and
                 complexity of solutions. The influence of selection of
                 different initial model components (genes) on the
                 probability of successful solution is studied in
                 detail. In one especially successful run of the GP
                 system the Siebel's expression was genetically
                 developed. In addition, redundancy of the knowledge
                 hidden in the experimental data was detected and
                 eliminated without the influence of human intelligence.
                 Researches showed excellent agreement between the
                 experimental data, existing analytical solutions, and
                 models obtained genetically.",
  notes =        "Journal of Intelligent Manufacturing
                 http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40528-70-35668245-0,00.html",
}

Genetic Programming entries for Miran Brezocnik Joze Balic Karl Kuzman

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