Abstract
The chapter summarizes the practical experience of integrating genetic programming and statistical modeling at The Dow Chemical Company. A unique methodology for using Genetic Programming in statistical modeling of designed and undesigned data is described and illustrated with successful industrial applications. As a result of the synergistic efforts, the building technique has been improved and the model development cost and time can be significantly reduced. In case of designed data Genetic Programming reduced costs by suggesting transformations as an alternative to doing additional experimentation. In case of undesigned data Genetic Programming was instrumental in reducing the model building costs by providing alternative models for consideration.
Keywords
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
Banzhaf, W., Nordin, P., Keller, R., and Francone, F. (1998). Genetic Programming: An Introduction, San Francisco, CA: Morgan Kaufmann.
Box, G., Hunter, W., and Hunter, J. (1978). Statistics for Experiments: An Introduction to Design, Data Analysis, and Model Building. New York, NY: Wiley.
Box, G. and Draper, N. (1987). Empirical Model Building and Response Surfaces. 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.
Castillo, F., Marshall, K, Greens, J. and Kordon, A. (2003). A Methodology for Combining Symbolic Regression and Design of Experiments to Improve Empirical Model Building In Proceedings of the Genetic and Evolutionary Computing Conference (GECCO’2003), E. Cantu-Paz, et al (Eds), pp. 1975–1985. Chicago, IL: Springer.
Castillo, F., Sweeney, J., and Zirk, W. (2004). Using Evolutionary Algorithms to Suggest Variable Transformations in Linear Model Lack-of-Fit Situations, accepted to CEC 2004.
Cawse, J. (2003). Experimental Design for Combinatorial and High Throughput Materials Development. New York, NY: Wiley.
Cook, R. (1977). Detection of Influential Observations in Linear Regression, Technometrics, 19: 15–18.
Draper, N. R. and Smith, H. (1981). Applied Regression Analysis (Second Edition). New York, NY: Wiley.
Harry, M. and Schroeder, R. (2000). Six Sigma: The Breakthrough Management Strategy Revolutionizing the World’s Top Corporations. New York, NY: Doubleday.
Hiden, H. G., Willis, M. J., and Montague, G.A (1999). Non-linear Principal Component Analysis Using Genetic Programming, Computers and Chemical Engineering 23, pp 413–425.
Kaboudan M.(1999). Statistical Evaluation of Genetic Programming, In Proceedings of the 5th International Conference on Computing in Economics and Finance (CEF’99), pp.24–26. Boston, MA.
Kordon, A., Smits, G., Kalos, A., and Jordaan, E. (2003). Robust Soft Sensor Development Using Genetic Programming, In Nature-Inspired Methods in Chemometrics, R. Leardi (Ed.), pp70–108. Amsterdam: Elsevier
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.
Montgomery, D and Peck, E. (1992). Introduction to Linear Regression Analysis. New York, NY: Wiley.
Montgomery, D. (1999) Design and Analysis of Experiments. New York, NY: Wiley.
Myers, R H., and Montgomery, D. (1995). Response Surface Methodology: Process and Product Optimization Using Designed Experiments. New York, NY: Wiley.
Spoonger, S. (2000). Using Factorial Experiments to Evaluate the Effects of Genetic Programming parameters. In Proceedings of EuroGP’2000, pp. 2782–2788. Edinburgh, UK
Seber, G.A., and Wild, C. J. (1989). Nonlinear Regression, pp. 5–7. John Wiley and Sons, New York.
Smits, G. and Kotanchek, M. (2004). Pareto-Front Exploitation in Symbolic Regression, Genetic Programming Theory and Practice, pp 283–300, R. Riolo and B. Worzel (Eds). Boston, MA: Kluwer.
Westbury, C, Buchanan, P., Sanderson, M., Rhemtulla, M., and Phillips, L. (2003). Using Genetic Programming to Discover Nonlinear Variable Interactions, Behavior Research Methods, Instruments, & Computers 35(2): 2020–216.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer Science+Business Media, Inc.
About this chapter
Cite this chapter
Castillo, F., Kordon, A., Sweeney, J., Zirk, W. (2005). Using Genetic Programming in Industrial Statistical Model Building. In: O’Reilly, UM., Yu, T., Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice II. Genetic Programming, vol 8. Springer, Boston, MA. https://doi.org/10.1007/0-387-23254-0_3
Download citation
DOI: https://doi.org/10.1007/0-387-23254-0_3
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-23253-9
Online ISBN: 978-0-387-23254-6
eBook Packages: Computer ScienceComputer Science (R0)