BookChapters.bib

@inbook{Lutton-TI2006,
  author = {Lutton, Evelyne},
  title = {Trait\'e d'informatique industrielle},
  chapter = {Algorithmes g\'en\'etiques et algorithmes
  évolutionnaires},
  publisher = {Techniques de l'ing\'enieur},
  year = {2006},
  month = {Juin},
  pages = {},
  note = {Dossier S7218},
  url = {http://www.techniques-ingenieur.fr/dossier/algorithmes\_genetiques\_et\_algorithmes\_evolutionnaires/S7218}
}
@inbook{REE-Appriou,
  author = {Lutton, Evelyne},
  title = {Gestion de la complexit\'e et de l'information dans les grands syst\`emes critiques, sous la direction de Alain Appriou},
  chapter = {La r\'esolution de probl\`emes complexes par \'evolution artificielle},
  publisher = {SEE, CNRS Editions},
  year = {2009},
  month = {Janvier},
  pages = {},
  note = {Collection Les monographies SEE},
  url = {http://www.see.asso.fr/htdocs/main.php/monographies.php/}
}
@inbook{Lutton-EUROGEN99,
  author = {Lutton, Evelyne},
  title = {Evolutionary Algorithms in Engineering and Computer Science},
  chapter = {Genetic Algorithms and Fractals},
  publisher = {John Wiley and Sons},
  year = {1999},
  key = {K. Mietttinen, M. M. M\"akel\"a, P. Neittaanmaki, J. P\'eriaux (Eds)}
}
@inbook{CLO-2008,
  author = {Cagnoni, Stefano and Lutton, Evelyne  and Olague, Gustavo},
  title = {Genetic and Evolutionary Computation for Image Processing and Analysis},
  chapter = {Genetic and Evolutionary Computation for Image Processing and Analysis. Introduction},
  publisher = {Hindawi},
  year = {2008},
  volume = {EURASIP Book Series on Signal Processing and Communications Volume 7},
  note = {ISBN 978-977-454-001-1},
  url = {http://www.hindawi.com/books/9789774540011/}
}
@inbook{LL-2008,
  author = {Lutton, Evelyne  and L\'evy V\'ehel, Jacques},
  title = {Genetic and Evolutionary Computation for Image Processing and Analysis},
  chapter = {Evolutionary multifractal signal/image denoising},
  publisher = {Hindawi},
  year = {2008},
  volume = {EURASIP Book Series on Signal Processing and Communications Volume 7},
  note = {ISBN 978-977-454-001-1},
  url = {http://www.hindawi.com/books/9789774540011/}
}
@inbook{LM-1995,
  author = {Lutton, Evelyne  and Martinez, Patrice},
  title = {Artificial Evolution: European Conference AE 95, Brest, France, September 4-6, 1995 Selected Papers},
  chapter = {A genetic algorithm with sharing for the detection of 2D geometric primitives in images},
  publisher = {Springer-Verlag },
  year = {1996},
  volume = {1063},
  pages = {287-303},
  note = {Lecture Notes in Computer Science},
  url = {http://www.springerlink.com/content/y0v5853164022724/}
}
@inbook{EVOLVE2011,
  author = {Barri\`ere, Olivier and  Lutton, Evelyne and Wuillemin,  Pierre-Henri and Baudrit, C\'edric and  Sicard, Mariette and  Perrot, Nathalie},
  title = {EVOLVE - A bridge between Probability, Set Oriented Numerics and Evolutionary Computation},
  chapter = {Cooperative coevolution for agrifood process modeling},
  publisher = {Springer-Verlag},
  year = {2012},
  volume = {},
  pages = {},
  abstract = {On the contrary to classical schemes of evolutionary optimisations algorithms,
single population Cooperative Co-evolution techniques (CCEAs, also called
"Parisian" approaches) make it possible to represent the evolved solution as an aggregation
of several individuals (or even as a whole population). In other words,
each individual represents only a part of the solution. This scheme allows simulating
the principles of Darwinian evolution in a more economic way, which results
in gain in robustness and efficiency. The counterpart however is a more complex
design phase. In this chapter, we detail the design of efficient CCEAs schemes on
two applications related to the modeling of an industrial agri-food process. The experiments
correspond to complex optimisations encountered in the modeling of a
Camembert-cheese ripening process. Two problems are considered:
 (i) A deterministic modeling problem, phase prediction, for which a search for a
closed form tree expression is performed using genetic programming (GP).
 (ii) A Bayesian network structure estimation problem. The novelty of the proposed
approach is based on the use of a two step process based on an intermediate representation
called independence model. The search for an independence model
is formulated as a complex optimisation problem, for which the CCEA scheme
is particularly well suited. A Bayesian network is finally deduced using a deterministic
algorithm, as a representative of the equivalence class figured by the
independence model.},
  note = {Studies in Computational Intelligence},
  pdf = {Papers/INCALIN-Evolve-4.pdf}
}

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