On the Application of Genetic Programming to Chemical
Process Systems
B. McKay, M. Willis, und G. Barton. 1995 IEEE Conference on Evolutionary Computation, 2, Seite 701--706. Perth, Australia, IEEE Press, (29 November - 1 December 1995)
Zusammenfassung
In this contribution a genetic programming approach is
used to develop mathematical models of chemical process
systems. Having discussed genetic programming in
general, two examples are used to reveal the utility of
the technique. It is shown how the method can
discriminate between relevant and irrelevant process
inputs, evolving to yield parsimonious model structures
that accurately represent process characteristics. This
removes the need for restrictive assumptions about the
form of the data and the structure of the required
model. In addition, as the technique determines complex
nonlinear relationships in the data, non-intuitive
process features are revealed with comparative ease.
%0 Conference Paper
%1 mckay:1995:cps
%A McKay, Ben
%A Willis, Mark J.
%A Barton, Geoffrey W.
%B 1995 IEEE Conference on Evolutionary Computation
%C Perth, Australia
%D 1995
%I IEEE Press
%K algorithms, genetic programming
%P 701--706
%T On the Application of Genetic Programming to Chemical
Process Systems
%V 2
%X In this contribution a genetic programming approach is
used to develop mathematical models of chemical process
systems. Having discussed genetic programming in
general, two examples are used to reveal the utility of
the technique. It is shown how the method can
discriminate between relevant and irrelevant process
inputs, evolving to yield parsimonious model structures
that accurately represent process characteristics. This
removes the need for restrictive assumptions about the
form of the data and the structure of the required
model. In addition, as the technique determines complex
nonlinear relationships in the data, non-intuitive
process features are revealed with comparative ease.
@inproceedings{mckay:1995:cps,
abstract = {In this contribution a genetic programming approach is
used to develop mathematical models of chemical process
systems. Having discussed genetic programming in
general, two examples are used to reveal the utility of
the technique. It is shown how the method can
discriminate between relevant and irrelevant process
inputs, evolving to yield parsimonious model structures
that accurately represent process characteristics. This
removes the need for restrictive assumptions about the
form of the data and the structure of the required
model. In addition, as the technique determines complex
nonlinear relationships in the data, non-intuitive
process features are revealed with comparative ease.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Perth, Australia},
author = {McKay, Ben and Willis, Mark J. and Barton, Geoffrey W.},
biburl = {https://www.bibsonomy.org/bibtex/25b7b858f1db768dce17939ad9d1b4d3c/brazovayeye},
booktitle = {1995 IEEE Conference on Evolutionary Computation},
interhash = {fa22383c4a9a4bfe2cf48b4848950f77},
intrahash = {5b7b858f1db768dce17939ad9d1b4d3c},
keywords = {algorithms, genetic programming},
month = {29 November - 1 December},
notes = {ICEC-95 Editors not given by IEEE, Organisers David
Fogel and Chris deSilva.
conference details at
http://ciips.ee.uwa.edu.au/~dorota/icnn95.html},
pages = {701--706},
publisher = {IEEE Press},
publisher_address = {Piscataway, NJ, USA},
timestamp = {2008-06-19T17:46:37.000+0200},
title = {On the Application of Genetic Programming to Chemical
Process Systems},
volume = 2,
year = 1995
}