Generation of structured process models using Genetic Programming
The design of structured mathematical models of processes in a certain level of abstraction defined by the given task appears to be difficult and time consuming even for experienced experts.
This paper reports on a new method for the design of structured process models based on the metaphor of Genetic Programming. This new methodology allows the automatic generation of non-linear process models in a self-organizing way.
Keywordsgenetic programming modelling system identification process models structured models structure optimization parameter optimization control systems learning control industrial application biotechnology SMOG Matlab Smulink
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- Bettenhausen, K.D., Marenbach, P., Freyer, S., Rettenmaier, H. andNieken, U.: Self-organizing structured modelling of a biotechnological fed-batch fermentation by means of genetic programming. First IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, 12–14 September 1995, Conference Publication No. 414, pp. 481–486, 1995.Google Scholar
- Fonseca, C. M. and Fleming P. J.: Genetic Algorithms for Multiple Objective Optimization: Formulation, Discussion and Generalization. Proceedings of the Fifth International Conference on Genetic Algorithms and their Application, pp. 416–423, San Mateo, California, USA: Morgan Kaufmann Publishers, 1993.Google Scholar
- Goldberg, D. E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, Mass.: Addison-Wesley, 1989.Google Scholar
- Hooke, R. and Jeeves, T.A.: Direct search: Solution of numerical and statistical problems. Journal of the Association of Computing Machinery, pp. 212–224, 1961.Google Scholar
- Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press: Cambridge, Massachusetts, 1992.Google Scholar
- Marenbach, P., Bettenhausen, K.D. and Cuno, B.: Selbstorganisierende Generierung strukturierter Prozeßmodelle. at-Automatisierungstechnik 6 (1995), pp. 277–288, Berlin, 1995.Google Scholar
- Pohlheim, H.: Ein genetischer Algorithmus mit Mehrfachpopulationen zur Numerischen Optimierung, at-Automatisierungstechnik 3 (1995), pp. 127–135, Berlin, 1995.Google Scholar
- Schwefel, H.-P.: Numerical optimization of computer models. Chichester: Wiley & Sons, 1981.Google Scholar