Abstract
Intelligent optimization is a domain of evolutionary computation that emerges since a few years. All the methods within this discipline are based on mechanisms for maintaining a set of individuals and, separately, a space of knowledge linked to the individuals. The aim is to make the individuals evolve to reach better solutions generation after generation using the knowledge linked to them. The idea proposed in this paper consists in using previous experiences in order to build the knowledge referential and then accelerate the search process. A method which allows reusing knowledge gained from experience feedback is proposed. This approach has been applied to the problem of selection of project scenario in a multi-objective context. An evolutionary algorithm has been modified in order to allow the reuse of capitalized knowledge. This knowledge is gathered in an influence diagram allowing its reuse by the algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Talbi, D.: Application of Optimization Techniques to Parameter Set-up of Industrial Scheduling Software. Computers In Industry 55(2), 105–124 (2005)
Michalski, R.S., Wojtusiak, J., Kaufman, K.A.: Intelligent Optimization via Learnable Evolution Model. In: 18th IEEE Conf. on Tools with Artificial Intelligence, pp. 332–335 (2006)
Russel, S., Norvig, P.: Artificial Intelligence: A modern approach, 2nd edn. Prentice Hall/Pearson education international, London (2003)
Huyet, A.-L., Paris, J.-L.: Synergy between Evolutionary Optimization and Induction Graphs Learning for Simulated Manufacturing Systems. International Journal of Production Research 42(20), 4295–4313 (2004)
Sebag, M., Schoenauer, M.: A rule based similarity measure, vol. 837, pp. 119–130. Springer, Heidelberg (1994)
Cervone, K.: Experimental Validations of the Learnable Evolution Model. Congress on Evolutionary Computation, San Diego CA (2000)
Alami, J., El imrani, A.: A multipopulation cultural algorithm using fuzzy clustering. Applied Soft Computing 7(2), 506–519 (2007)
Chung, C.J.: Knowledge based approaches to self adaptation in cultural algorithms, PhD thesis, Wayne State University, Detroit, USA (1997)
Chebel-Morello, B., Lereno, E., Baptiste, P.: A New Algorithm to Select Learning Examples from Learning Data. In: Leung, K.-S., Chan, L., Meng, H. (eds.) IDEAL 2000. LNCS, vol. 1983, pp. 13–15. Springer, Heidelberg (2000)
Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms. A new Tool for Evolutionary Computation. Kluwer, Dordrecht (2001)
Miquélez, M., Bengoetxea, E., Larrañaga, P.: Evolutionary computation based on Bayesian classifiers. Int. Journal AMCS 14(3), 335–349 (2004)
Baron, C., Rochet, S., Esteve, D.: GESOS: a multi-objective genetic tool for project management considering technical and non-technical constraints, Art. Intel. Applications and Innovations (AIAI), IFIP World Computer Congress, Toulouse (2004)
Zitzler, E., Thiele, L.: Multi objective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. on evolutionary computation 3, n°4, 257–271 (1999)
Watthayu, W., Peng, Y.: A Bayesian network based framework for multi-criteria decision making. In: MCDM 2004, Whistler, Canada (2004)
Becker, A., Naim, P.: Les Réseaux Bayésien: modèles graphique de connaissance, Eyrolles (1999)
Vareilles, E., Aldanondo, M.: Evaluation of a Solution in Interactive Aiding Design Process. In: INCOM 2006, Saint Etienne, France (2006)
Crampes, M.: Méta modèle adaptatif de la pertinence d’un modèle de connaissance. In: RFIA 2004, Toulouse (2004)
Amilastre, J., Fargier, H., Marquis, P.: Consistency restoration and explanations in dynamic CSPs – Application to configuration, vol. 135(1-2), pp. 199–234. Elsevier A.I, Amsterdam (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pitiot, P., Coudert, T., Geneste, L., Baron, C. (2008). Improvement of Intelligent Optimization by an Experience Feedback Approach. In: Monmarché, N., Talbi, EG., Collet, P., Schoenauer, M., Lutton, E. (eds) Artificial Evolution. EA 2007. Lecture Notes in Computer Science, vol 4926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79305-2_27
Download citation
DOI: https://doi.org/10.1007/978-3-540-79305-2_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79304-5
Online ISBN: 978-3-540-79305-2
eBook Packages: Computer ScienceComputer Science (R0)