- author = "Mark Kotanchek and Guido Smits and Arthur Kordon",
- title = "Industrial Strength Genetic Programming",
- booktitle = "Genetic Programming Theory and Practice",
- publisher = "Kluwer",
- year = "2003",
- editor = "Rick L. Riolo and Bill Worzel",
- chapter = "15",
- pages = "239--255",
- keywords = "genetic algorithms, genetic programming, Empirical Modeling, Symbolic Regression, Support Vector Machines, SVM, ANN, Neural Networks",
- ISBN = "1-4020-7581-2",
-
URL = "
http://www.springer.com/computer/ai/book/978-1-4020-7581-0",
-
URL = "
http://www.evolved-analytics.com/.../GPTP03_IndustrialGP_Preprint.pdf",
-
DOI = "
10.1007/978-1-4419-8983-3_15",
- 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",
-
notes = "In theory, there is no difference between theory and
practice. In practice, there is. -- Jan L.A. van de
Snepscheut
Dowchemical Part of \cite{RioloWorzel:2003}",
- size = "18 pages",
