Abstract
This work concentrates on three different modifications of a genetic programming system for symbolic regression analysis. The coefficient of correlation R 2 is used as fitness function instead of the mean squared error and offspring selection is used to ensure a steady improvement of the achieved solutions. Additionally, as the fitness evaluation consumes most of the execution time, the generated solutions are only evaluated on parts of the training data to speed up the whole algorithm. These three algorithmic adaptations are incorporated in the symbolic regression algorithm and their impact is tested on two real world datasets describing a blast furnace and a temper mill process. The effect on the achieved solution quality as well as on the produced models are compared to results generated by a symbolic regression algorithm without the mentioned modifications and the benefits are highlighted.
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
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)
Andahazy, D., Slaby, S., Löffler, G., Winter, F., Feilmayr, C., Bürgler, T.: Governing processes of gas and oil injection into the blast furnace. ISIJ International 46(4), 496–502 (2006)
Cho, S., Cho, Y., Yoon, S.: Reliable roll force prediction in cold mill using multiple neural networks 8(4), 874–882 (1997)
Keijzer, M.: Improving symbolic regression with interval arithmetic and linear scaling. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 70–82. Springer, Heidelberg (2003)
Kommenda, M., Kronberger, G., Affenzeller, M., Winkler, S., Feilmayr, C., Wagner, S.: Symbolic regression with sampling. In: 22nd European Modeling and Simulation Symposium EMSS 2010, Fes, Morocco, pp. 13–18 (October 2010)
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 IEEE 2nd International Symposium on Logistics and Industrial Informatics (Lindi 2009), Linz, Austria, pp. 58–62 (September 2009)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)
Kronberger, G., Feilmayr, C., Kommenda, M., Winkler, S., Affenzeller, M., Thomas, B.: System identification of blast furnace processes with genetic programming. In: Proceedings of the IEEE 2nd International Symposium on Logistics and Industrial Informatics (Lindi 2009), Linz, Austria, pp. 63–68 (September 2009)
Luke, S.: Two fast tree-creation algorithms for genetic programming. IEEE Transactions on Evolutionary Computation 4(3), 274–283 (2000)
Radhakrishnan, V.R., Mohamed, A.R.: Neural networks for the identification and control of blast furnace hot metal quality. Journal of Process Control 10(6), 509–524 (2000)
Stelzer, R., Pútz, P.D., Diegelmann, V., Gorgels, F., Piesack, D.: Optimum temper rolling degree: Pre-set and influencing effects of bending deformations. Steel research international 76(2-3), 105–110 (2005)
Vladislavleva, E.: Model-based problem solving through symbolic regression via pareto genetic programming. Open Access publications from Tilburg University urn:nbn:nl:ui:12-3125460, Tilburg University (2008), http://ideas.repec.org/p/ner/tilbur/urnnbnnlui12-3125460.html
Wagner, S.: Heuristic Optimization Software Systems - Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment. Ph.D. thesis, Institute for Formal Models and Verification, Johannes Kepler University, Linz, Austria (2009)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kommenda, M. et al. (2012). Application of Symbolic Regression on Blast Furnace and Temper Mill Datasets. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27549-4_51
Download citation
DOI: https://doi.org/10.1007/978-3-642-27549-4_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27548-7
Online ISBN: 978-3-642-27549-4
eBook Packages: Computer ScienceComputer Science (R0)