Advertisement

Application Issues of Genetic Programming in Industry

  • Arthur Kordon
  • Flor Castillo
  • Guido Smits
  • Mark Kotanchek
Part of the Genetic Programming book series (GPEM, volume 9)

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.

Keywords

Genetic programming symbolic regression industrial applications design of experiments real world problems parameter selection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Box, G., Hunter, W., and Hunter, J. (1978). Statistics for Experiments: An Introduction to Design, Data Analysis, and Model Building, New York, NY: Wiley.Google Scholar
  2. 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.Google Scholar
  3. 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, UKGoogle Scholar
  4. 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.Google Scholar
  5. 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.Google Scholar
  6. 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.Google Scholar
  7. 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.Google Scholar
  8. 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: ElsevierGoogle Scholar
  9. 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.Google Scholar
  10. 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.Google Scholar
  11. Koza, J. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection, Cambridge, MA: MIT Press.Google Scholar
  12. 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.Google Scholar
  13. Montgomery, D. (1999) Design and Analysis of Experiments, New York, NY: Wiley.Google Scholar
  14. Predictive Modeling Markup Language (PMML V 3.0) Specification, (2004) Data Mining Group, http://www.dmg.org/pmml-v3-0.Google Scholar
  15. 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.Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Arthur Kordon
    • 1
  • Flor Castillo
    • 1
  • Guido Smits
    • 2
  • Mark Kotanchek
    • 3
  1. 1.The Dow Chemical CompanyFreeport
  2. 2.Dow BeneluxTemeuzenThe Netherlands
  3. 3.Evolved AnalyticsMidland

Personalised recommendations