A Genetic Programming Approach for Construction of Surrogate Models
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{FERREIRA:2019:PICFCPD,
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author = "Jimena Ferreira and Martin Pedemonte and
Ana I. Torres",
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title = "A Genetic Programming Approach for Construction of
Surrogate Models",
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booktitle = "Proceedings of the 9th International Conference on
Foundations of Computer-Aided Process Design",
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year = "2019",
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editor = "Salvador Garcia Munoz and Carl D. Laird and
Matthew J. Realff",
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series = "Computer Aided Chemical Engineering",
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volume = "47",
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pages = "451--456",
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address = "Copper Mountain, Colorado, USA",
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keywords = "genetic algorithms, genetic programming, Surrogate
Models, Response Surface Models",
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publisher = "Elsevier",
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ISSN = "1570-7946",
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URL = "http://www.sciencedirect.com/science/article/pii/B9780128185971500722",
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DOI = "doi:10.1016/B978-0-12-818597-1.50072-2",
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size = "6 pages",
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abstract = "Surrogate models, response surface models or
meta-models are `lack-'ox models that describe a system
with high accuracy. We present a methodology that
combines iterative Design of experiments (DOE) with
Genetic Programming (GP) in order to obtain surrogate
models. GP is an evolutionary technique to create
computer programs. In the context of surrogate
modelling. the programs are possible functional forms
of the model, that are used to fit experimental data.
Therefore, unlike most approaches, non-linear
combinations of the basis functions are possible. The
iterative DOE provides a methodology to choose data
points to test current programs and build the next
generation. Data is obtained from Aspen Plus based
simulations and the process of data acquisition is
automatized via Python. The methodology is applied to a
RadFrac distillation column which is part of a corn to
ethanol process and considers three input and three
output variables. The results indicate that the
proposed methodology is able to provide accurate
surrogate models for the variables",
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notes = "Instituto de Computacion, Facultad de Ingenieria,
Universidad de la Republica, Montevideo, Uruguay,
11300",
- }
Genetic Programming entries for
Jimena Ferreira
Martin Pedemonte
Ana Ines Torres
Citations