Modeling human expertise on a cheese ripening industrial process using GP
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InProceedings{Barriere:2008:PPSN,
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author = "Olivier Barriere and Evelyne Lutton and
Cedric Baudrit and Mariette Sicard and Bruno Pinaud and
Nathalie Perrot",
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title = "Modeling human expertise on a cheese ripening
industrial process using GP",
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booktitle = "Parallel Problem Solving from Nature - PPSN X",
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year = "2008",
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editor = "Gunter Rudolph and Thomas Jansen and Simon Lucas and
Carlo Poloni and Nicola Beume",
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volume = "5199",
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series = "LNCS",
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pages = "859--868",
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address = "Dortmund",
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month = "13-17 " # sep,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-87699-5",
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url_fake = "http://metronum.futurs.inria.fr/html/Papers/files/pdf/Barriere_18-06-2008_INCALIN-PPSN2008-Final.pdf",
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DOI = "doi:10.1007/978-3-540-87700-4_85",
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size = "10 pages",
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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.",
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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