Modeling human expertise on a cheese ripening industrial process using GP

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Barriere:2008:PPSN,
  author =       "Olivier Barriere and Evelyne Lutton and 
                 Cedric Baudrit and Mariette Sicard and Bruno Pinaud and 
                 Nathalie Perrot",
  title =        "Modeling human expertise on a cheese ripening
                 industrial process using GP",
  booktitle =    "Parallel Problem Solving from Nature - PPSN X",
  year =         "2008",
  editor =       "Gunter Rudolph and Thomas Jansen and Simon Lucas and 
                 Carlo Poloni and Nicola Beume",
  volume =       "5199",
  series =       "LNCS",
  pages =        "859--868",
  address =      "Dortmund",
  month =        "13-17 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-87699-5",
  url_fake =     "http://metronum.futurs.inria.fr/html/Papers/files/pdf/Barriere_18-06-2008_INCALIN-PPSN2008-Final.pdf",
  DOI =          "doi:10.1007/978-3-540-87700-4_85",
  size =         "10 pages",
  abstract =     "Industrial agrifood processes often strongly rely on
                 human expertise, expressed as know-how and control
                 procedures based on subjective measurements (colour,
                 smell, texture), which are very difficult to capture
                 and model. We deal in this paper with a cheese ripening
                 process (of French Camembert), for which experimental
                 data have been collected within a cheese ripening
                 laboratory chain. A global and a monopopulation
                 cooperative/coevolutive GP scheme (Parisian approach)
                 have been developed in order to simulate phase
                 prediction (i.e. a subjective estimation of human
                 experts) from microbial proportions and Ph
                 measurements. These two GP approaches are compared to
                 Bayesian network modelling and simple multilinear
                 learning algorithms. Preliminary results show the
                 effectiveness and robustness of the Parisian GP
                 approach.",
  notes =        "GPLAB, Matlab, multi linear regression, INCALIN,
                 Terminals: time derivatives of pH acidity, lactose and
                 two bacteria concentrations. Gaussian random constants.
                 Function set: arithmetics, log, exp?, Boolean ops.
                 Fitness: parsimony, Euclidean sharing distance. tree
                 GP. 30-40 nodes. Mutation Chi squared. 16 experiments
                 each lasting 40 days. Missing data estimated by fitting
                 splines. Log? distribution on floats. See also
                 \cite{inria-00381681}

                 PPSN X",
}

Genetic Programming entries for Olivier Barriere Evelyne Lutton Cedric Baudrit Mariette Sicard Bruno Pinaud Nathalie Perrot

Citations