Modeling an agrifood industrial process using cooperative coevolution Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@TechReport{inria-00381681,

• title = "Modeling an agrifood industrial process using cooperative coevolution Algorithms",
• author = "Olivier Barriere and Evelyne Lutton and Pierre-Henri Wuillemin and Cedric Baudrit and Mariette Sicard and Bruno Pinaud and Nathalie Perrot",
• institution = "INRIA",
• year = "2009",
• number = "inria-00381681, version 1",
• address = "Parc Orsay, France",
• month = "6 " # may,
• keywords = "genetic algorithms, genetic programming, Parisian, Computer Science, Artificial Intelligence, Life Sciences/Food and Nutrition, Agrifood, Cheese ripening, Cooperative coevolution, Parisian approach, Bayesian Network",
• URL = "http://hal.inria.fr/inria-00381681/en/",
• URL = "http://hal.inria.fr/docs/00/38/16/81/PDF/RR2008.pdf",
• bibsource = "OAI-PMH server at oai.archives-ouvertes.fr",
• identifier = "HAL:inria-00381681, version 1",
• language = "EN",
• oai = "oai:hal.archives-ouvertes.fr:inria-00381681_v1",
• abstract = "This report presents two experiments related to the modeling of an industrial agrifood process using evolutionary techniques. Experiments have been focused on a specific problem which is the modeling of a Camembert-cheese ripening process. Two elated complex optimisation problems have been considered: -- a deterministic modeling problem, the phase prediction problem, for which a search for a closed form tree expression has been performed using genetic programming (GP), -- a Bayesian network structure estimation problem, considered as a two-stage problem, i.e. searching first for an approximation of an independence model using EA, and then deducing, via a deterministic algorithm, a Bayesian network which represents the equivalence class of the independence model found at the first stage. In both of these problems, cooperative-coevolution techniques (also called ``Parisian'' approaches) have been proved successful. These approaches actually allow to represent the searched solution as an aggregation of several individuals (or even as a whole population), as each individual only bears a part of the searched solution. This scheme allows to use the artificial Darwinism principles in a more economic way, and the gain in terms of robustness and efficiency is important.",
• size = "51 pages",

}

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

Citations