Regressor Survival Rate Estimation for Enhanced Crossover Configuration

  • Alina Patelli
  • Lavinia Ferariu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6593)


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.


genetic operators fuzzy control schema theory nonlinear systems identification multiobjective optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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)zbMATHGoogle Scholar
  2. 2.
    Nelles, O.: Nonlinear System Identification – From Classical Approaches to Neural Networks and Fuzzy Models. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  3. 3.
    Poli, R., McPhee, N.F.: General Schema Theory for Genetic Programming with Subtree-Swapping Crossover: Part 2. Evol. Comp. 11(2), 169–206 (2003)CrossRefGoogle Scholar
  4. 4.
    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) Google Scholar
  5. 5.
    Patelli, A., Ferariu, L.: Increasing Crossover Operator Efficiency in Multiobective Nonlinear Systems Identification. In: Proc. of IEEE Intelligent Systems Conference, pp. 426–431 (2010)Google Scholar
  6. 6.
    Coello Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  7. 7.
    De Jong, K.A.: Evolutionary Computation – A Unified Approach. MIT Press, Cambridge (2006)zbMATHGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Deb, K.: Multiobjective Optimization Using Evolutionary Algorithms. Wiley&Sons, Chichester (2001)zbMATHGoogle Scholar
  12. 12.
    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)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alina Patelli
    • 1
  • Lavinia Ferariu
    • 1
  1. 1.Department of Automatic Control and Applied Informatics“Gh. Asachi” Technical University of IasiIasiRomania

Personalised recommendations