A Comparison of Evolutionary Computing Techniques Used to Model Bi-Directional Reflectance Distribution Functions. Banks, E. R., Nunez, E., Agarwal, P., McBride, M., Liedel, R., & Owens, C. In Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2006), Seattle, WA, USA, 8-12 July, 2006. Paper abstract bibtex Bi-Directional Reflectance Distribution Functions are used in many fields including computer animation modelling, military defence (radar, lidar, etc.), and others. This paper explores a variety of approaches to modelling BRDFs using different evolutionary computing (EC) techniques. We concentrate on genetic programming (GP) and in hybrid GP approaches, obtaining very close correspondence between models and data. The problem of obtaining parameters that make particular BRDF models fit to laboratory-measured reflectance data is a classic symbolic regression problem. The goal of this approach is to discover the equations that model laboratory-measured data according to several criteria of fitness. These criteria involve closeness of fit, simplicity or complexity of the model (parsimony), form of the result, and speed of discovery. As expected, free form, unconstrained GP gave the best results in terms of minimising measurement errors. However, it also yielded the most complex model forms. Certain constrained approaches proved to be far superior in terms of speed of discovery. Furthermore, application of mild parsimony pressure resulted in not only simpler expressions, but also improved results by yielding small differences between the models and the corresponding laboratory measurements.
@inproceedings{Banks:gecco06lbp,
abstract = {Bi-Directional Reflectance Distribution Functions are
used in many fields including computer animation
modelling, military defence (radar, lidar, etc.), and
others. This paper explores a variety of approaches to
modelling BRDFs using different evolutionary computing
(EC) techniques. We concentrate on genetic programming
(GP) and in hybrid GP approaches, obtaining very close
correspondence between models and data. The problem of
obtaining parameters that make particular BRDF models
fit to laboratory-measured reflectance data is a
classic symbolic regression problem. The goal of this
approach is to discover the equations that model
laboratory-measured data according to several criteria
of fitness. These criteria involve closeness of fit,
simplicity or complexity of the model (parsimony), form
of the result, and speed of discovery. As expected,
free form, unconstrained GP gave the best results in
terms of minimising measurement errors. However, it
also yielded the most complex model forms. Certain
constrained approaches proved to be far superior in
terms of speed of discovery. Furthermore, application
of mild parsimony pressure resulted in not only simpler
expressions, but also improved results by yielding
small differences between the models and the
corresponding laboratory measurements.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Seattle, WA, USA},
author = {Banks, Edwin Roger and Nunez, Edwin and Agarwal, Paul and McBride, Marshall and Liedel, Ronald and Owens, Claudette},
biburl = {https://www.bibsonomy.org/bibtex/2ea0f1b7593ff36305acd4e5cae764817/brazovayeye},
booktitle = {Late breaking paper at Genetic and Evolutionary
Computation Conference {(GECCO'2006)}},
editor = {Grahl, J{\"{o}}rn},
interhash = {296634c9cfd2bdab8e5cd5f7e6670365},
intrahash = {ea0f1b7593ff36305acd4e5cae764817},
keywords = {genetic algorithms, programming},
month = {8-12 July},
notes = {Distributed on CD-ROM at GECCO-2006},
timestamp = {2008-06-19T17:36:11.000+0200},
title = {A Comparison of Evolutionary Computing Techniques Used
to Model Bi-Directional Reflectance Distribution
Functions},
url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/lbp128.pdf},
year = 2006
}
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The goal of this approach is to discover the equations that model laboratory-measured data according to several criteria of fitness. These criteria involve closeness of fit, simplicity or complexity of the model (parsimony), form of the result, and speed of discovery. As expected, free form, unconstrained GP gave the best results in terms of minimising measurement errors. However, it also yielded the most complex model forms. Certain constrained approaches proved to be far superior in terms of speed of discovery. 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