Abstract
Genetic Programming (GP) can be used to identify the nonlinear differential equations of dynamical systems. If, however, the fitness function is chosen in a classical way, the optimization will not work very well. In this article, we explain the reasons for the failure of the GP approach and present a solution strategy for improving performance. Using more than one identification criterion (fitness function) and switching based on the information content of the data enable standard GP algorithms to find better solutions in shorter times. A computational example illustrates that identification criteria switching has a bigger influence on the results than the choice of the GP parameters has.
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References
Babovic, V., Keijzer, M.: Evolutionary algorithms approach to induction of differential equations. In: Proceedings of the Fourth International Conference on Hydroinformatics, Iowa City, USA (2000)
Burgess, G.: Finding Approximate Analytic Solutions To Differential Equations Using Genetic Programming, Salisbury, Australia, vol. 5108 (1999)
Cao, H., Kang, L., Chen, Y.: Evolutionary Modeling of Ordinary Differential Equations for Dynamic Systems. In: Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, vol. 2, pp. 959–965. Morgan Kaufmann, San Francisco (1999)
Gagne, C., Parizeau, M.: Open BEAGLE: A New C++ Evolutionary Computation Framework (2002)
Gagne, C., Parizeau, M.: Open BEAGLE: A New Versatile C++ Framework for Evolutionary Computations (2002)
Gray, G.J., Murray-Smith, D.J., Li, Y., Sharman, K.C., Weinbrenner, T.: Nonlinear model structure identification using genetic programming. Control Engineering Practice 6, 1341–1352 (1998)
Haykin, S.: Neural Networks - A Comprehensive Foundation, 2nd edn., pp. 215–217. Prentice Hall International Inc., Englewood Cliffs (1999)
Juang, J.N.: Applied system identification. Prentice-Hall, Inc., Upper Saddle River (1994)
Keijzer, M., Babovic, V.: Dimensionally Aware Genetic Programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, vol. 2, pp. 1069–1076. Morgan Kaufmann, San Francisco (1999)
Koza, J.R.: Genetic programming: On the programming of computers by natural selection. MIT Press, Cambridge (1992)
Ljung, L.: System Identification: Theory for the User (Second Ed.). Prentice-Hall, Inc, Upper Saddle River (1998)
Nelles, O.: Nonlinear System Identification, pp. 182–184. Springer, Berlin (2001)
Rodriguez-Vazquez, K., Fonseca, C.M., Fleming, P.J.: Multiobjective Genetic Programming: A Nonlinear System Identification Application. In: Late Breaking Papers at the 1997 Genetic Programming Conference, CA, USA, pp. 207–212. Stanford Bookstore, Stanford University (1997)
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Buchsbaum, T., Vössner, S. (2006). Information-Dependent Switching of Identification Criteria in a Genetic Programming System for System Identification. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds) Genetic Programming. EuroGP 2006. Lecture Notes in Computer Science, vol 3905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11729976_27
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DOI: https://doi.org/10.1007/11729976_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33143-8
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