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Industrial Strength Genetic Programming

Empirical Modeling and Symbolic Regression via GP: Integrated Methodologies, Best Practices, Lessons Learned

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Part of the book series: Genetic Programming 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

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© 2003 Springer Science+Business Media New York

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Kotanchek, M., Smits, G., Kordon, A. (2003). Industrial Strength Genetic Programming. In: Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice. Genetic Programming Series, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8983-3_15

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  • DOI: https://doi.org/10.1007/978-1-4419-8983-3_15

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-4747-7

  • Online ISBN: 978-1-4419-8983-3

  • eBook Packages: Springer Book Archive

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