Evolutionary Identification of Macro-Mechanical Models

Created by W.Langdon from gp-bibliography.bib Revision:1.4472

  author =       "Marc Schoenauer and Michele Sebag and 
                 Francois Jouve and Bertrand Lamy and Habibou Maitournam",
  title =        "Evolutionary Identification of Macro-Mechanical
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "467--488",
  chapter =      "23",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming, structural
  ISBN =         "0-262-01158-1",
  URL =          "http://citeseer.ist.psu.edu/cache/papers/cs/902/http:zSzzSzwww.eeaax.polytechnique.frzSzpaperszSzmarczSzAGP2.pdf/schoenauer96evolutionary.pdf",
  URL =          "http://citeseer.ist.psu.edu/155790.html",
  URL =          "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277524",
  size =         "22 pages",
  abstract =     "This chapter illustrates the potential of genetic
                 programming (GP) in the field of macro-mechanical
                 modelling, addressing the problem of identification of
                 a mechanical model for a material. Two kinds of models
                 are considered. One-dimensional dynamic models are
                 represented via symbolic formulations termed
                 rheological models, which are directly evolved by GP.
                 Three-dimensional static models of hyperelastic
                 materials are expressed in terms of strain energy
                 functions. A model is rated based on the distance
                 between the behaviour predicted by the model, and the
                 actual behavior of the material given by a set of
                 mechanical experiments. The choice of GP is motivated
                 by strong arguments, relying on the tree-structure of
                 rheological models in the first case, and on the need
                 for first and second order derivatives in the second
                 case. Key issues are the exploration of viable
                 individuals only, and the use of Gaussian mutations to
                 optimise numerical constants.",

Genetic Programming entries for Marc Schoenauer Michele Sebag Francois Jouve Bertrand Lamy Habibou Maitournam