Abstract
In the framework of nonlinear systems identification by means of multiobjective genetic programming, the paper introduces a customized crossover operator, guided by fuzzy controlled regressor encapsulation. The approach is aimed at achieving a balance between exploration and exploitation by protecting well adapted subtrees from division during recombination. To reveal the benefits of the suggested genetic operator, the authors introduce a novel mathematical formalism which extends the Schema Theory for cut point crossover operating on trees encoding regressor based models. This general framework is afterwards used for monitoring the survival rates of fit encapsulated structural blocks. Other contributions are proposed in answer to the specific requirements of the identification problem, such as a customized tree building mechanism, enhanced elite processing and the hybridization with a local optimization procedure. The practical potential of the suggested algorithm is demonstrated in the context of an industrial application involving the identification of a subsection within the sugar factory of Lublin, Poland.
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.
References
Fogel, D.B.: Evolutionary Computation – Towards a new Philosophy of Machine Intelligence, 3rd edn. IEEE Press Series on Computational Intelligence. IEEE Press, Los Alamitos (2006)
Nelles, O.: Nonlinear System Identification – From Classical Approaches to Neural Networks and Fuzzy Models. Springer, Heidelberg (2001)
Poli, R., McPhee, N.F.: General Schema Theory for Genetic Programming with Subtree-Swapping Crossover: Part 2. Evol. Comp. 11(2), 169–206 (2003)
Patelli, A., Ferariu, L.: Dynamic Fuzzy Controlled Regressor Encapsulation in Evolving Nonlinear Models. In: 14th Int. Conf. on System Theory and Control, pp. 373–378 (2010)
Patelli, A., Ferariu, L.: Increasing Crossover Operator Efficiency in Multiobective Nonlinear Systems Identification. In: Proc. of IEEE Intelligent Systems Conference, pp. 426–431 (2010)
Coello Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, Heidelberg (2007)
De Jong, K.A.: Evolutionary Computation – A Unified Approach. MIT Press, Cambridge (2006)
Ferariu, L., Patelli, A.: Multiobjective Genetic Programming for Nonlinear Systems Identification. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds.) ICANNGA 2009. LNCS, vol. 5495, pp. 233–242. Springer, Heidelberg (2009)
Rachmawati, L., Srinivasan, D.: Multiobjective Evolutionary Algorithm with Controllable Focus on the Knees of the Pareto Front. IEEE Trans. Evol. Comp. 13(4), 810–824 (2009)
Adra, S.F., Dodd, T.J., Griffin, I.A., Fleming, P.J.: Convergence Acceleration Operator for Multiobjective Optimization. IEEE Trans. on Evol. Comp. 13(4), 825–847 (2009)
Deb, K.: Multiobjective Optimization Using Evolutionary Algorithms. Wiley&Sons, Chichester (2001)
Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective Optimisation with Messy Genetic Algorithms. In: Proc. of the 2000 ACM Symposium on Applied Computing, p. 470 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Patelli, A., Ferariu, L. (2011). Regressor Survival Rate Estimation for Enhanced Crossover Configuration. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-20282-7_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20281-0
Online ISBN: 978-3-642-20282-7
eBook Packages: Computer ScienceComputer Science (R0)