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Using Genetic Programming in Nonlinear Model Identification

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Part of the Lecture Notes in Control and Information Sciences book series (LNCIS,volume 418)

Abstract

In this paper we summarize the use of genetic programming (GP) in nonlinear system identification: After giving a short introduction to evolutionary computation and genetic algorithms, we describe the basic principles of genetic programming and how it is used for data based identification of nonlinear mathematical models. Furthermore, we summarize projects in which we have successfully applied GP in R&D projects in the last years; we also give a summary of several algorithmic enhancements that have been successfully researched in the last years (including offspring selection, on-line and sliding window GP, operators for monitoring genetic process dynamics, and the design of cooperative evolutionary data mining agents). A short description of HeuristicLab (HL), the optimization framework developed by the HEAL research group, and the use of the GP implementations in HL are given in the appendix of this paper.

Keywords

  • Genetic Algorithm
  • Diesel Engine
  • Genetic Programming
  • Heuristic Optimization
  • Algorithmic Enhancement

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Affenzeller, M., Wagner, S.: Offspring selection: A new self-adaptive selection scheme for genetic algorithms. In: Ribeiro, B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds.) Adaptive and Natural Computing Algorithms. Springer Computer Series, pp. 218–221. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  2. Affenzeller, M., Wagner, S., Winkler, S.: GA-selection revisited from an ES-driven point of view. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 262–271. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  3. Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. Numerical Insights. CRC Press, Boca Raton (2009)

    Google Scholar 

  4. Alberer, D., del Re, L., Winkler, S., Langthaler, P.: Virtual sensor design of particulate and nitric oxide emissions in a DI diesel engine. In: Proceedings of the 7th International Conference on Engines for Automobile ICE 2005 (2005)

    Google Scholar 

  5. del Re, L., Langthaler, P., Furtmüller, C., Winkler, S., Affenzeller, M.: NOx virtual sensor based on structure identification and global optimization. In: Proceedings of the SAE World Congress 2005 (2005)

    Google Scholar 

  6. Holland, J.H.: Adaption in Natural and Artifical Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  7. Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 97–106 (2001)

    Google Scholar 

  8. Kommenda, M., Kronberger, G., Winkler, S., Affenzeller, M., Wagner, S., Schickmair, L., Lindner, B.: Application of genetic programming on temper mill datasets. In: Proceedings of the 2nd International Symposium on Logistics and Industrial Informatics LINDI 2009. IEEE Publications, Los Alamitos (2009)

    Google Scholar 

  9. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  10. Kronberger, G., Winkler, S., Affenzeller, M., Beham, A., Wagner, S.: On the success rate of crossover operators for genetic programming with offspring selection. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2009. LNCS, vol. 5717, pp. 793–800. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  11. Kronberger, G., Winkler, S., Affenzeller, M., Wagner, S.: Data mining via distributed genetic programming agents. In: Bruzzone, A., Longo, F., Piera, M.A., Aguilar, R.M., Frydman, C. (eds.) Proceedings of the 20th European Modeling and Simulation Symposium (EMSS 2008), pp. 95–99. DIPTEM University of Genova (2008)

    Google Scholar 

  12. Kronberger, G., Winkler, S., Affenzeller, M., Wagner, S.: On crossover success rate in genetic programming with offspring selection. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 232–243. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  13. Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2002)

    MATH  CrossRef  Google Scholar 

  14. Ljung, L.: System Identification – Theory For the User, 2nd edn. PTR Prentice Hall, Upper Saddle River (1999)

    Google Scholar 

  15. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1992)

    MATH  CrossRef  Google Scholar 

  16. Morrison, F.: The Art of Modeling Dynamic Systems: Forecasting for Chaos, Randomness, and Determinism. John Wiley & Sons, Inc., Chichester (1991)

    MATH  Google Scholar 

  17. Rechenberg, I.: Evolutionsstrategie. Friedrich Frommann Verlag (1973)

    Google Scholar 

  18. Schwefel, H.-P.: Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie. Birkhäuser Verlag, Basel (1994)

    Google Scholar 

  19. Wagner, S.: Heuristic Optimization Software Systems - Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment. PhD thesis, Johannes Kepler University, Linz, Austria, (2009)

    Google Scholar 

  20. Wagner, S., Affenzeller, M.: SexualGA: Gender-specific selection for genetic algorithms. In: Callaos, N., Lesso, W., Hansen, E. (eds.) Proceedings of the 9th World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI) 2005, vol. 4, pp. 76–81. International Institute of Informatics and Systemics (2005)

    Google Scholar 

  21. Wagner, S., Kronberger, G., Beham, A., Winkler, S., Affenzeller, M.: Modeling of heuristic optimization algorithms. In: Bruzzone, A., Longo, F., Piera, M.A., Aguilar, R.M., Frydman, C. (eds.) Proceedings of the 20th European Modeling and Simulation Symposium, pp. 106–111. DIPTEM University of Genova (2008)

    Google Scholar 

  22. Wagner, S., Winkler, S., Pitzer, E., Kronberger, G., Beham, A., Braune, R., Affenzeller, M.: Benefits of plugin-based heuristic optimization software systems. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds.) EUROCAST 2007. LNCS, vol. 4739, pp. 747–754. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  23. Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23(2), 69–101 (1996)

    Google Scholar 

  24. Winkler, S.: Evolutionary System Identification - Modern Approaches and Practical Applications. PhD thesis, Johannes Kepler University, Linz, Austria (2008)

    Google Scholar 

  25. Winkler, S.: Evolutionary System Identification - Modern Approaches and Practical Applications. Schriften der Johannes Kepler Universität Linz, Reihe C: Technik und Naturwissenschaften, Universitätsverlag Rudolf Trauner (2009)

    Google Scholar 

  26. Winkler, S., Affenzeller, M., Jacak, W., Stekel, H.: Classification of tumor marker values using heuristic data mining methods. In: Proceedings of the GECCO 2010 Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC 2010), Portland, OR. Association for Computing Machinery (ACM), New York (2010)

    Google Scholar 

  27. Winkler, S., Affenzeller, M., Wagner, S.: Genetic programming based model structure identification using on-line system data. In: International Mediterranean Modeling Multiconference, Proceedings of Conceptual Modeling and Simulation Conference, CMS 2005, pp. 177–186 (2005)

    Google Scholar 

  28. Winkler, S., Affenzeller, M., Wagner, S.: New methods for the identification of nonlinear model structures based upon genetic programming techniques. Journal of Systems Science 31(1), 5–13 (2005)

    Google Scholar 

  29. Winkler, S., Affenzeller, M., Wagner, S.: Advanced genetic programming based machine learning. Journal of Mathematical Modelling and Algorithms 6(3), 455–480 (2007)

    MathSciNet  MATH  CrossRef  Google Scholar 

  30. Winkler, S., Affenzeller, M., Wagner, S.: Selection pressure driven sliding window behavior in genetic programming based structure identification. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds.) EUROCAST 2007. LNCS, vol. 4739, pp. 788–795. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  31. Winkler, S., Affenzeller, M., Wagner, S.: Off spring selection and its effects on genetic propagation in genetic programming based system identification. In: Trappl, R. (ed.) Cybernetics and Systems 2008, vol. 2, pp. 549–554. Austrian Society for Cybernetic Studies (2008)

    Google Scholar 

  32. Winkler, S., Affenzeller, M., Wagner, S.: Variables diversity in systems identification based on extended genetic programming. Journal of Systems Science 34(2), 27–34 (2008)

    MATH  Google Scholar 

  33. Winkler, S., Affenzeller, M., Wagner, S.: On the reliability of nonlinear modeling using enhanced genetic programming techniques. In: Skiadas, C.H., Dimotikalis, I., Skiadas, C. (eds.) Topics on Chaotic Systems: Selected Papers from Chaos 2008 International Conference, pp. 24–31. World Scientific Publishing, Singapore (2009)

    Google Scholar 

  34. Winkler, S., Affenzeller, M., Wagner, S.: Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis. Genetic Programming and Evolvable Machines 10(2), 111–140 (2009)

    CrossRef  Google Scholar 

  35. Winkler, S., Efendic, H., Affenzeller, M., Del Re, L., Wagner, S.: On-line modeling based on genetic programming. International Journal on Intelligent Systems Technologies and Applications 2(2/3), 255–270 (2007)

    CrossRef  Google Scholar 

  36. Winkler, S., Efendic, H., del Re, L.: Quality pre-assesment in steel industry using data based estimators. In: Cierpisz, S., Miskiewicz, K., Heyduk, A. (eds.) Proceedings of the IFAC Workshop MMM 2006 on Automation in Mining, Mineral and Metal Industry, International Federation for Automatic Control (2006)

    Google Scholar 

  37. Winkler, S., Hirsch, M., Affenzeller, M., del Re, L., Wagner, S.: Incorporating physical knowledge about the formation of nitric oxides into evolutionary system identification. In: Bruzzone, A., Longo, F., Piera, M.A., Aguilar, R.M., Frydman, C. (eds.) Proceedings of the 20th European Modeling and Simulation Symposium (EMSS 2008), pp. 69–74. DIPTEM University of Genova (2008)

    Google Scholar 

  38. Winkler, S.M., Hirsch, M., Affenzeller, M., del Re, L., Wagner, S.: Virtual sensors for emissions of a diesel engine produced by evolutionary system identification. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2009. LNCS, vol. 5717, pp. 657–664. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

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Winkler, S., Affenzeller, M., Wagner, S., Kronberger, G., Kommenda, M. (2012). Using Genetic Programming in Nonlinear Model Identification. In: Alberer, D., Hjalmarsson, H., del Re, L. (eds) Identification for Automotive Systems. Lecture Notes in Control and Information Sciences, vol 418. Springer, London. https://doi.org/10.1007/978-1-4471-2221-0_6

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  • DOI: https://doi.org/10.1007/978-1-4471-2221-0_6

  • Publisher Name: Springer, London

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