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)


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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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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