Evolutionary Generation and Refinement of Mathematical Process Models

Created by W.Langdon from gp-bibliography.bib Revision:1.1944

@InProceedings{Freyeretal1998,
  author =       "Stephan Freyer and J{\"o}rg Graefe and 
                 Matthias Heinzel and Peter Marenbach",
  address =      "Aachen, Germany",
  booktitle =    "Eufit '98, 6th European Congress on Intelligent
                 Techniques and Soft Computing, ELITE - European
                 Laboratory for Intelligent TechniquesEngineering",
  editor =       "Hans-J{\"u}rgen Zimmermann",
  pages =        "1471--1475",
  title =        "Evolutionary Generation and Refinement of Mathematical
                 Process Models",
  volume =       "III",
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming, SMOG,
                 bioprocess, modelling",
  URL =          "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_98_08.pdf",
  URL =          "http://www.rt.e-technik.tu-darmstadt.de/LIT",
  size =         "5 page",
  email =        "pmarenbach@gmx.net",
  abstract =     "Modelling of biotechnological processes is generally
                 difficult and time consuming. In order to generate
                 mathematical models of a studied reaction system in a
                 short time period a new modelling technique for the
                 optimisation of structures, based on the principles of
                 evolution, was developed. This method generates
                 transparent and comprehensible dynamic models in a data
                 driven manner. In addition it is able to automatically
                 refine sub-models or to verify model ideas. The
                 transparent mathematical form of the generated models
                 is a major advantage giving the user the opportunity to
                 interpret the model and to influence the modelling
                 process interactively. The article at hand presents two
                 examples of biotechnological processes for which this
                 new method was successfully applied.",
  notes =        "http://www.eufit.org/proceedings/98/volume3.htm

                 BASF AG laboratories, high noise. Monod,
                 SubLimTeissier, SubLimJost, SubInhAnstrews, SubInhWebb
                 MATLAB/SIMULINK. Stresses importance of user
                 understandable models, using prior knowledge, parsimony
                 versus accuracy (trade off in fitness function). Batch
                 fed fermentation.

                 ",
}

Genetic Programming entries for Stephan Freyer J\"org Graefe Matthias Heinzel Peter Marenbach