Abstract
This chapter gives a systematic view, based on the experience from The Dow Chemical Company, of the key issues for applying symbolic regression with Genetic Programming (GP) in industrial problems. The competitive advantages of GP are defined and several industrial problems appropriate for GP are recommended and referenced with specific applications in the chemical industry. A systematic method for selecting the key GP parameters, based on statistical design of experiments, is proposed. The most significant technical and non-technical issues for delivering a successful GP industrial application are discussed briefly.
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References
Box, G., Hunter, W., and Hunter, J. (1978). Statistics for Experiments: An Introduction to Design, Data Analysis, and Model Building, New York, NY: Wiley.
Castillo, F., Marshall, K, Greens, J. and Kordon, A. (2002). Symbolic Regression in Design of Experiments: A Case Study with Linearizing Transformations, In Proceedings of the Genetic and Evolutionary Computing Conference (GECCO’2002), W. Langdon, et al (Eds), pp. 1043–1048. New York, NY: Morgan Kaufmann.
Feldt R. and Nordin P. (2000). Using Factorial Experiments to Evaluate the Effects of Genetic Programming parameters. In Proceedings of EuroGP’2000, pp. 271–282, Edinburgh, UK
Kalos A., Kordon, A, Smits, G., and Werkmeister, S. (2003) Hybrid Model Development Methodology for Industrial Soft Sensors, In Proceedings of the American Control Conference (ACC’2003), pp. 5417–5422, Denver. CO.
Kordon A. and Smits, G. (2001) Soft Sensor Development Using Genetic Programming, In Proceedings of the Genetic and Evolutionary Computing Conference (GECCO’2001), L. Spector, et al (Eds), pp. 1346–1351, San Francisco, Morgan Kaufmann.
Kordon A., H. Pham, C. Bosnyak, M. Kotanchek, and G. Smits, (2002). Accelerating Industrial Fundamental Model Building with Symbolic Regression: A Case Study with Structure — Property Relationships, In Proceedings of the Genetic and Evolutionary Computing Conference (GECCO’2002), D. Davis and R. Roy (Eds), Volume Evolutionary Computation in Industry, pp. 111–116. New York, NY: Morgan Kaufmann.
Kordon A., Kalos, A. and Adams, B. (2003a), Empirical Emulators for Process Monitoring and Optimization, In Proceedings of the IEEE 11thConference on Control and Automation MED’2003, pp.111, Rhodes, Greece.
Kordon, A., Smits, G., Kalos, A., and Jordaan, E. (2003b). Robust Soft Sensor Development Using Genetic Programming, In Nature-Inspired Methods in Chemometrics, (R. Leardi-Editor), Amsterdam: Elsevier
Kordon A. and Lue, C. (2004) Symbolic Regression Modeling of Blown Film Process Effects, In Proceedings of the Congress of Evolutionary Computation CEC’2004, pp. 561–568, Portland, OR.
Kotanchek, M, Smits, G. and Kordon, A. (2003). Industrial Strength Genetic Programming, In Genetic Programming Theory and Practice, pp 239–258, R. Riolo and B. Worzel (Eds), Boston, MA: Kluwer.
Koza, J. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection, Cambridge, MA: MIT Press.
Jordaan, E., Kordon, A., Smits, G., and Chiang, L. (2004), Robust Inferential Sensors based on Ensemble of predictors generated by Genetic Programming, In Proceedings of PPSN 2004, pp. 522–531, Birmingham, UK.
Montgomery, D. (1999) Design and Analysis of Experiments, New York, NY: Wiley.
Predictive Modeling Markup Language (PMML V 3.0) Specification, (2004) Data Mining Group, http://www.dmg.org/pmml-v3-0.
Smits, G. and Kotanchek, M. (2004), Pareto-Front Exploitation in Symbolic Regression, Genetic Programming Theory and Practice, pp 283–300, U.M. O’Reilly, T. Yu, R. Riolo and B. Worzel (Eds), Boston, MA: Springer.
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Kordon, A., Castillo, F., Smits, G., Kotanchek, M. (2006). Application Issues of Genetic Programming in Industry. In: Yu, T., Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice III. Genetic Programming, vol 9. Springer, Boston, MA. https://doi.org/10.1007/0-387-28111-8_16
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DOI: https://doi.org/10.1007/0-387-28111-8_16
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