@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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