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Industrial Strength Genetic Programming

Empirical Modeling and Symbolic Regression via GP: Integrated Methodologies, Best Practices, Lessons Learned
  • Mark Kotanchek
  • Guido Smits
  • Arthur Kordon
Part of the Genetic Programming Series book series (GPEM, volume 6)

Abstract

Since the mid-1990’s, symbolic regression via genetic programming (GP) has become a core component of a multi-disciplinary approach to empirical modeling at Dow Chemical. Herein we review the role of symbolic regression within an integrated empirical modeling methodology, discuss symbolic regression system design issues, best practices and lessons learned from industrial application, and present future directions for research and application

Key words

Genetic Programming Empirical Modeling Symbolic Regression Support Vector Machines Neural Networks 

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References

  1. Banzhaf, W., Nordin, P., Keller, R. and Francone, F. (1998). Genetic Programming - An Introduction. Morgan Kaufmann, San Francisco, CA.zbMATHGoogle Scholar
  2. Castillo, F. A., Marshall, K., Green, J. and Kordon, A. K. (2002). Symbolic Regression in Design of Experiments: A Case Study with Linearizing Transformations. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), W. B. Langdon, et al. (Eds. ), pp. 1043–1048. New York: Morgan Kaufmann.Google Scholar
  3. Hastie, T., Tibshirani, R. and Friedman, J. (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer-Verlag.zbMATHGoogle Scholar
  4. Jacob, C. (2001). Illustrating Evolutionary Computation with Mathematica. San Francisco: Morgan Kaufmann.Google Scholar
  5. Jordaan, E. Maria. (2002). Development of Robust Inferential Sensors: Industrial Application of Support Vector Machines for Regression. Eindhoven: Universiteitsdrukkerij TU Eindhoven.Google Scholar
  6. Kordon, A. K., Pham, H. T., Bosnyak, C. P. and Kotanchek, M. E. (2002). Accelerating Indus-trial Fundamental Model Building with Symbolic Regression: A Case Study with Structure- Property Relationships. In GECCO 2002: Presentations in the Evolutionary Compution in Industry Track, D. Davis and R. Roy (Eds. ), pp. 111–116. New York: GECCO 2002 Conference.Google Scholar
  7. Kordon, A. K. and Smits, G. F. (2001). Soft Sensor Development using Genetic Programming. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), L. Spector, et al. (Eds. ), pp. 1346–1351. New York, Morgan Kaufmann.Google Scholar
  8. Kotanchek, M. E., et al. (2002). Evolutionary Computing in Dow Chemical. In GECCO 2002 Presentations in the Evolutionary Computation in Industry Track, D. Davis and R. Roy (Eds. ), pp. 101–110. New York: GECCO 2002 Conference.Google Scholar
  9. Kotanchek, M. E., (2003). Industrial Strength Symbolic Regression: Evolving Empirical Models from Industrial Data. 2003 Mathematica Developers Conference, Champaign, IL: Presentation.Google Scholar
  10. Mercure, P. Kip., Smits, G. F. and Kordon, A. K. (2001). Empirical Emulators for First Principle Models. Fall 2001 AIChE Meeting. Reno: Presentation.Google Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Mark Kotanchek
    • 1
  • Guido Smits
    • 2
  • Arthur Kordon
    • 3
  1. 1.Dow ChemicalMidlandUSA
  2. 2.Dow BeneluxTerneuzenUSA
  3. 3.Dow ChemicalFreeportUSA

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