[1] 
Olivier Barrière, Evelyne Lutton, PierreHenri Wuillemin, Cédric Baudrit,
Mariette Sicard, and Nathalie Perrot.
EVOLVE  A bridge between Probability, Set Oriented Numerics and
Evolutionary Computation, chapter Cooperative coevolution for agrifood
process modeling.
SpringerVerlag, 2012.
Studies in Computational Intelligence.
[ bib 
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On the contrary to classical schemes of evolutionary optimisations algorithms, single population Cooperative Coevolution 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 agrifood process. The experiments correspond to complex optimisations encountered in the modeling of a Camembertcheese 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.

[2]  Evelyne Lutton. Gestion de la complexité et de l'information dans les grands systèmes critiques, sous la direction de Alain Appriou, chapter La résolution de problèmes complexes par évolution artificielle. SEE, CNRS Editions, Janvier 2009. Collection Les monographies SEE. [ bib  http ] 
[3]  Stefano Cagnoni, Evelyne Lutton, and Gustavo Olague. Genetic and Evolutionary Computation for Image Processing and Analysis, volume EURASIP Book Series on Signal Processing and Communications Volume 7, chapter Genetic and Evolutionary Computation for Image Processing and Analysis. Introduction. Hindawi, 2008. ISBN 9789774540011. [ bib  http ] 
[4]  Evelyne Lutton and Jacques Lévy Véhel. Genetic and Evolutionary Computation for Image Processing and Analysis, volume EURASIP Book Series on Signal Processing and Communications Volume 7, chapter Evolutionary multifractal signal/image denoising. Hindawi, 2008. ISBN 9789774540011. [ bib  http ] 
[5]  Evelyne Lutton. Traité d'informatique industrielle, chapter Algorithmes génétiques et algorithmes évolutionnaires. Techniques de l'ingénieur, Juin 2006. Dossier S7218. [ bib  http ] 
[6]  Evelyne Lutton. Evolutionary Algorithms in Engineering and Computer Science, chapter Genetic Algorithms and Fractals. John Wiley and Sons, 1999. [ bib ] 
[7]  Evelyne Lutton and Patrice Martinez. Artificial Evolution: European Conference AE 95, Brest, France, September 46, 1995 Selected Papers, volume 1063, chapter A genetic algorithm with sharing for the detection of 2D geometric primitives in images, pages 287303. SpringerVerlag, 1996. Lecture Notes in Computer Science. [ bib  http ] 
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