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
%0 Book Section
%1 kotanchek:2003:GPTP
%A Kotanchek, Mark
%A Smits, Guido
%A Kordon, Arthur
%B Genetic Programming Theory and Practise
%D 2003
%E Riolo, Rick L.
%E Worzel, Bill
%I Kluwer
%K ANN, Empirical Machines, Modeling, Networks Neural Regression, SVM, Support Symbolic Vector algorithms, genetic programming,
%P 239--256
%T Industrial Strength Genetic Programming
%X 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
%& 15
%@ 1-4020-7581-2
@incollection{kotanchek:2003:GPTP,
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},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Kotanchek, Mark and Smits, Guido and Kordon, Arthur},
biburl = {https://www.bibsonomy.org/bibtex/23e6db93e7049cd83d20d06eeb9886ffc/brazovayeye},
booktitle = {Genetic Programming Theory and Practise},
chapter = 15,
editor = {Riolo, Rick L. and Worzel, Bill},
interhash = {b23d562f15a0dfbd4ddf6e67aa4f547c},
intrahash = {3e6db93e7049cd83d20d06eeb9886ffc},
isbn = {1-4020-7581-2},
keywords = {ANN, Empirical Machines, Modeling, Networks Neural Regression, SVM, Support Symbolic Vector algorithms, genetic programming,},
notes = {In theory, there is no difference between theory and
practice. In practice, there is. -- Jan L.A. van de
Snepscheut
Dowchemical},
pages = {239--256},
publisher = {Kluwer},
size = {18 pages},
timestamp = {2008-06-19T17:43:42.000+0200},
title = {Industrial Strength Genetic Programming},
year = 2003
}