Modeling an agrifood industrial process using cooperative coevolution Algorithms - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Reports (Research Report) Year : 2009

Modeling an agrifood industrial process using cooperative coevolution Algorithms


This report presents two experiments related to the modeling of an industrial agrifood process using evolutionary techniques. Experiments have been focussed 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 roblem, 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.
Fichier principal
Vignette du fichier
RR2008.pdf (919.08 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

inria-00381681 , version 1 (06-05-2009)


  • HAL Id : inria-00381681 , version 1
  • PRODINRA : 249719


Olivier Barrière, Evelyne Lutton, Pierre-Henri Wuillemin, Cédric Baudrit, Mariette Sicard, et al.. Modeling an agrifood industrial process using cooperative coevolution Algorithms. [Research Report] RR-6914, INRIA. 2009, pp.51. ⟨inria-00381681⟩
304 View
256 Download


Gmail Mastodon Facebook X LinkedIn More