Skip to main content
SpringerLink
Log in

Cooperative Coevolution for Agrifood Process Modeling

  • Chapter
EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 447))

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:

  • A deterministic modeling problem, phase prediction, for which a search for a closed form tree expression is performed using genetic programming (GP).

  • 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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aldarf, M., Fourcade, F., Amrane, A., Prigent, Y.: Substrate and metabolite diffusion within model medium for soft cheese in relation to growth of penicillium camembertii. J. Ind. Microbiol. Biotechnol. 33, 685–692 (2006)

    Article  Google Scholar 

  2. Arfi, K., Amrita, F., Spinnler, H.E., Bonnarme, P.: Catabolism of volatile sulfur compounds precursors by brevibacterium linens and geotrichum candidum, two microorganisms of the cheese ecosystem. J. Biotechnol. 105(3), 245–253 (2003)

    Article  Google Scholar 

  3. Tucker, A., Liu, X.: Extending evolutionary programming methods to the learning of dynamic bayesian networks. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 1999), Orlando, Florida, USA. Morgan Kaufmann (July 1999)

    Google Scholar 

  4. Barile, D., Coisson, J.D., Arlorio, M., Rinaldi, M.: Identification of production area of ossolano italian cheese with chemometric complex aproach. Food Control 17(3), 197–206 (2006)

    Article  Google Scholar 

  5. Barrière, O., Lutton, E.: Experimental analysis of a variable size mono-population cooperative-coevolution strategy. In: Proceedings of International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO 2008), Puerto de La Cruz, Tenerife (November 2008)

    Google Scholar 

  6. Baudrit, C., Wuillemin, P.-H., Sicard, M., Perrot, N.: A Dynamic Bayesian Network to Represent a Ripening Process of a Soft Mould Cheese. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part II. LNCS (LNAI), vol. 5178, pp. 265–272. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  7. Binder, J., Koller, D., Russell, S., Kanazawa, K.: Adaptive probabilistic networks with hidden variables. Machine Learning 29, 213–244 (1997)

    Article  MATH  Google Scholar 

  8. Bongard, J., Lipson, H.: Active coevolutionary learning od deterministic finite automata. Journal of Machine Learning Research 6, 1651–1678 (2005)

    MathSciNet  MATH  Google Scholar 

  9. Boutrou, R., Guguen, M.: Interests in geotrichum candidum for cheese technology. Int. J. Food Microbiol. 102, 1–20 (2005)

    Article  Google Scholar 

  10. Bucci, A., Pollack, J.B.: On identifying global optima in cooperative coevolution. In: GECCO 2005: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pp. 539–544. ACM, New York (2005)

    Chapter  Google Scholar 

  11. Cheng, J., Bell, D.A., Liu, W.: Learning belief networks from data: An information theory based approach. In: Proceedings of Sixth ACM International Conference on Information and Knowledge Management, Las Vegas, Nevada, USA, pp. 325–331 (November 1997)

    Google Scholar 

  12. Chickering, D.M., Boutilier, C.: Learning equivalence classes of bayesian-network structures. Journal of Machine Learning Research, 150–157 (1996)

    Google Scholar 

  13. Chickering, D.M., Heckerman, D., Meek, C.: Large-sample learning of bayesian networks is np-hard. Journal of Machine Learning Research 5, 1287–1330 (2004)

    MathSciNet  MATH  Google Scholar 

  14. Choisy, C., Desmazeaud, M.J., Gripon, J.C., Lamberet, G., Lenoir, J.: La biochimie de l’affinage. In: Le Fromage, pp. 86–105. Lavoisier, Paris (1997)

    Google Scholar 

  15. Choisy, C., Desmazeaud, M.J., Gueguen, M., Lenoir, J., Schmidt, J.L., Tourneur, C.: Les phénomènes microbiens. In: Le Fromage, pp. 86–105. Lavoisier, Paris (1997)

    Google Scholar 

  16. Chow, C., Liu, C.: Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory 14(3), 462–467 (1968)

    Article  MathSciNet  MATH  Google Scholar 

  17. Collet, P., Lutton, E., Raynal, F., Schoenauer, M.: Polar ifs + parisian genetic programming = efficient ifs inverse problem solving. Genetic Programming and Evolvable Machines Journal 1(4), 339–361 (2000)

    Article  MATH  Google Scholar 

  18. Cooper, G.F., Herskovits, E.: A bayesian method for the induction of probabilistic networks from data. Machine Learning 9(4), 309–347 (1992)

    MATH  Google Scholar 

  19. Davis, L.: Adapting operators probabilities in genetic algorithms. In: Proceedings of the Third Conference on Genetic Algorithms, pp. 61–69. Morgan-Kaufmann (June 1989)

    Google Scholar 

  20. Deb, K., Goldberg, D.E.: An investigation of niche and species formation in genetic function optimization. In: Proceedings of the Third Conference on Genetic Algorithms, pp. 42–50 (June 1989)

    Google Scholar 

  21. Ellis, D.I., Broadhurst, D., Goodacre, R.: Rapid and quantitative detection of the microbial spoilage of beef by fourier transform infrared spectroscopy and machine learning. Analytica Chimica Acta 514(2), 193–201 (2004)

    Article  Google Scholar 

  22. Eriksson, R., Olsson, B.: Cooperative coevolution in inventory control optimisation. In: Proceedings of the Third International Conference on Artificial Neural Networks and Genetic Algorithms, East Lansing, MI, USA. Springer (July 1997)

    Google Scholar 

  23. Francois, O., Leray, P.: Etude comparative d’algorithmes d’apprentissage de structure dans les réseaux bayésiens. Technical report. Rencontres des Jeunes Chercheurs en IA (2003)

    Google Scholar 

  24. Friedman, N.: Learning belief networks in the presence of missing values and hidden variables. In: Proceedings of 14th International Conference on Machine Learning, Nashville, Tenessee, USA, pp. 125–133. Morgan Kaufmann (July 1997)

    Google Scholar 

  25. Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using bayesian network to analyze expression data. J. Computational Biology 7, 601–620 (2000)

    Article  Google Scholar 

  26. Goldberg, D.E., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: Proceedings of Second International Conference on Genetic Algorithms and Their Application, Cambridge, MA, USA, pp. 41–49. Lawrence Erlbaum Associates, Inc. (July 1987)

    Google Scholar 

  27. Gripon, A.: Mould-ripened cheeses. In: Cheese: Chemistry, Physics and Microbiology, pp. 111–136. Chapman and Hall, London (1993)

    Chapter  Google Scholar 

  28. Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. SIGART Bull. 63, 49–49 (1977)

    Article  Google Scholar 

  29. Husbands, P.: Distributed coevolutionary genetic algorithms for multi-criteria and multi-constraint optimisation. In: Selected Papers from AISB Workshop on Evolutionary Computing, London, UK, pp. 150–165. Springer (April 1994)

    Google Scholar 

  30. Husbands, P., Mill, F.: Simulated co-evolution as the mechanism for emergent planning and scheduling. In: Proceedings of the Fourth International Conference on Genetic Algorithms, San Diego, CA, USA, pp. 264–270. Morgan Kaufman (July 1991)

    Google Scholar 

  31. Ioannou, I., Mauris, G., Trystram, G., Perrot, N.: Back-propagation of imprecision in a cheese ripening fuzzy model based on human sensory evaluations. Fuzzy Sets And Systems 157, 1179–1187 (2006)

    Article  MathSciNet  Google Scholar 

  32. Ioannou, I., Perrot, N., Curt, C., Mauris, G., Trystram, G.: Development of a control system using the fuzzy set theory applied to a browning process - a fuzzy symbolic approach for the measurement of product browning: development of a diagnosis model - part i. Journal of Food Engineering 64, 497–506 (2004)

    Article  Google Scholar 

  33. Ioannou, I., Perrot, N., Mauris, G., Trystram, G.: Development of a control system using the fuzzy set theory applied to a browning process - towards a control system of the browning process combining a diagnosis model and a decision model - part ii. Journal of Food Engineering 64, 507–514 (2004)

    Article  Google Scholar 

  34. Jia, H., Liu, D., Yu, P.: Learning dynamic bayesian network with immune evolutionary algorithm. In: Proceedings of Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, China (August 2005)

    Google Scholar 

  35. Jimenez-Marquez, S.A., Thibault, J., Lacroix, C.: Prediction of moisture in cheese of commercial production using neural networks. Int. Dairy J. 15, 1156–1174 (2005)

    Article  Google Scholar 

  36. De Jong, E.D., Stanley, K.O., Wiegand, R.P.: Introductory tutorial on coevolution. In: GECCO 2007: Proceedings of the 2007 GECCO Conference Companion on Genetic and Evolutionary Computation, London, UK (July 2007)

    Google Scholar 

  37. Larranaga, P., Poza, M.: Structure learning of bayesian networks by genetic algorithms: A performance analysis of control parameters. IEEE Journal on Pattern Analysis and Machine Intelligence 18(9), 912–926 (1996)

    Article  Google Scholar 

  38. Leclercq-Perlat, M.N., Buono, F., Lambert, D., Latrille, E., Spinnler, H.E., Corrieu, G.: Controlled production of camembert-type cheeses. part i: Microbiological and physicochemical evolutions. J. Dairy Res. 71, 346–354 (2004)

    Article  Google Scholar 

  39. Leclercq-Perlat, M.N., Picque, D., Riahi, H., Corrieu, G.: Microbiological and biochemical aspects of camembert-type cheeses depend on atmospheric composition in the ripening chamber. J. Dairy Sci. 89, 3260–3273 (2006)

    Article  Google Scholar 

  40. Lenoir, J.: The surface flora and its role in the ripening of cheese. Int. Dairy Fed. Bull. 171, 3–20 (1984)

    Google Scholar 

  41. Louchet, J., Guyon, M., Lesot, M.J., Boumaza, A.M.: Dynamic flies: a new pattern recognition tool applied to stereo sequence processing. Pattern Recognition Letters 23, 335–345 (2002)

    Article  MATH  Google Scholar 

  42. Lutton, E., Martinez, P.: A genetic algorithm with sharing for the detection of 2d geometric primitives in images. In: AE 1995: Selected Papers from the European Conference on Artificial Evolution, Lille, France, pp. 287–303 (October 1995)

    Google Scholar 

  43. Moriarty, D.E., Miikkulainen, R.: Forming neural networks through efficient and adaptive coevolution. Evolutionary Computation 5, 373–399 (1998)

    Article  Google Scholar 

  44. Murphy, K.: The bayes net toolbox for matlab. Computing Science and Statistics 33(2), 1024–1034 (2001)

    Google Scholar 

  45. Myers, J.W., Laskey, K.B., DeJong, K.A.: Learning bayesian networks from incomplete data using evolutionary algorithms. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 1999), Orlando, Florida, USA, vol. 1, pp. 458–465. Morgan Kaufmann (July 1999)

    Google Scholar 

  46. Ni, H.X., Gunasekaran, S.: Food quality prediction with neural networks. Food Technology 52, 60–65 (1998)

    Google Scholar 

  47. Ochoa, G., Lutton, E., Burke, E.: Cooperative royal road functions. In: Evolution Artificielle, Tours, France, pp. 29–31 (October 2007)

    Google Scholar 

  48. Panait, L., Luke, S., Harrison, J.F.: Archive-based cooperative coevolutionary algorithms. In: GECCO 2006: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, Seattle, Washington, USA (July 2006)

    Google Scholar 

  49. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann (1988)

    Google Scholar 

  50. Pearl, J., Verma, T.: A theory of inferred causation. In: Proceedings of Second International Conference on the Principles of Knowledge Representation and Reasoning, Cambridge, MA, USA (April 1991)

    Google Scholar 

  51. Pinaud, B., Baudrit, C., Sicard, M., Wuillemin, P.-H., Perrot, N.: Validation et enrichissement interactifs d’un apprentissage automatique des paramètres d’un réseau bayésien dynamique appliqué aux procédés alimentaires. In: Journées Francophone sur les Réseaux Bayésiens, Lyon, France (May 2008)

    Google Scholar 

  52. Popovici, E., De Jong, K.: The effects of interaction frequency on the optimization performance of cooperative coevolution. In: Proceedings of Genetic and Evolutionary Computation Conference, GECCO 2006, Seattle, Washington, USA (July 2006)

    Google Scholar 

  53. Potter, M.A., De Jong, K.: A Cooperative Coevolutionary Approach to Function Optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  54. Potter, M.A., De Jong, K.: Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 1–29 (2000)

    Article  Google Scholar 

  55. Potter, M.A., De Jong, K.: The Coevolution of Antibodies for Concept Learning. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 530–539. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  56. Riahi, M.H., Trelea, I.C., Leclercq-Perlat, M.N., Picque, D., Corrieu, G.: Model for changes in weight and dry matter during the ripening of a smear soft cheese under controlled temperature and relative humidity. International Dairy Journal 17, 946–953 (2000)

    Article  Google Scholar 

  57. Robinson, R.W.: Counting unlabeled acyclic digraphs. In: Combinatorial Mathematics V: Proceedings of the Fifth Australian Conference, Melbourne, Australia, pp. 28–43. Springer (2000)

    Google Scholar 

  58. Ross, J., Zuviria, E.: Evolving dynamic bayesian networks with multi-objective genetic algorithms. Applied Intelligence 26(1), 13–23 (2007)

    Article  Google Scholar 

  59. Silva, S.: GPLAB A Genetic Programming Toolbox for MATLAB (2008), http://gplab.sourceforge.net/

  60. Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. The MIT Press (2001)

    Google Scholar 

  61. Tarantilis, C.D., Kiranoudis, C.T.: Operational research and food logistics. Journal of Food Engineering 70(3), 253–255 (2005)

    Article  MathSciNet  Google Scholar 

  62. Tonda, A., Lutton, E., Squillero, G.: Lamps: A test problem for cooperative coevolution. In: NICSO 2011, the 5th International Workshop on Nature Inspired Cooperative Strategies for Optimization, Cluj Napoca, Romania, October 20-22 (2011)

    Google Scholar 

  63. Vidal, F.P., Lazaro-Ponthus, D., Legoupil, S., Louchet, J., Lutton, É., Rocchisani, J.-M.: Artificial Evolution for 3D PET Reconstruction. In: Collet, P., Monmarché, N., Legrand, P., Schoenauer, M., Lutton, E. (eds.) EA 2009. LNCS, vol. 5975, pp. 37–48. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  64. Wang, S.-C., Li, S.-P.: Learning Bayesian Networks by Lamarckian Genetic Algorithm and Its Application to Yeast Cell-Cycle Gene Network Reconstruction from Time-Series Microarray Data. In: Ijspeert, A.J., Murata, M., Wakamiya, N. (eds.) BioADIT 2004. LNCS, vol. 3141, pp. 49–62. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  65. Wiegand, R.P., Liles, W., De Jong, K.: Analyzing cooperative coevolution with evolutionary game theory. In: Proceedings of the 2002 Congress on Evolutionary Computation CEC 2002, Honolulu, Hawaii, pp. 1600–1605 (May 2000)

    Google Scholar 

  66. Wiegand, R.P., Potter, M.A.: Robustness in cooperative coevolution. In: GECCO 2006: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, Seattle, Washington, USA (July 2006)

    Google Scholar 

  67. Wong, M.L., Leung, K.S.: An efficient data mining method for learning bayesian networks using an evolutionary algorithm-based hybrid approach. IEEE Transactions on Evolutionary Computation 8, 378–404 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. INRIA Saclay - Ile-de-France, Parc Orsay Université, 4, rue Jacques Monod, 91893, Orsay Cedex, France

    Olivier Barrière & Evelyne Lutton

  2. LIP6-CNRS UMR7606, 75016, Paris, France

    Pierre-Henri Wuillemin

  3. UMR782 Génie et Microbiologie des Procédés Alimentaires. AgroParisTech, INRA, F-78850, Thiverval-Grignon, France

    Cédric Baudrit, Mariette Sicard & Nathalie Perrot

Authors
  1. Olivier Barrière
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Evelyne Lutton
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pierre-Henri Wuillemin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cédric Baudrit
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mariette Sicard
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nathalie Perrot
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Olivier Barrière .

Editor information

Editors and Affiliations

  1. Computer Science and Communications, Research Unit, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, L-1359, Luxembourg

    Emilia Tantar

  2. , Computer Science and Communications, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, 1359, Luxembourg

    Alexandru-Adrian Tantar

  3. Computer Science, and Communications, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, L-1359, Luxembourg

    Pascal Bouvry

  4. Bordeaux Mathematical Institute, INRIA Bordeaux-Sud Ouest, Université Bordeaux I, cours de la Libération 351, Talence Cedex, 33405, France

    Pierre Del Moral

  5. Bâtiment Leyteire, UFR Sciences et Modélisation, Université Bordeaux II, 3ter place de la Victoire, Bordeaux, 33000, France

    Pierrick Legrand

  6. , Computer Science Department, CINVESTAV-IPN, Col. San Pedro Zacatenco 2508, Mexico City, 07360, Mexico

    Carlos A. Coello Coello

  7. , Computer Science Department, CINVESTAV-IPN, Col. San Pedro Zacatenco 2508, Mexico City, 07360, Mexico

    Oliver Schütze

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Berlin Heidelberg

About this chapter

Cite this chapter

Barrière, O., Lutton, E., Wuillemin, PH., Baudrit, C., Sicard, M., Perrot, N. (2013). Cooperative Coevolution for Agrifood Process Modeling. In: Tantar, E., et al. EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation. Studies in Computational Intelligence, vol 447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32726-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32726-1_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32725-4

  • Online ISBN: 978-3-642-32726-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation