We used Linear Genetic Programming (LGP) to study the
extent to which automated learning techniques may be
used to improve Unexploded Ordinance (UXO)
discrimination from Protem-47 and Geonics EM61
non-invasive electromagnetic sensors. We conclude that:
(1) Even after geophysicists have analysed the EM61
signals and ranked anomalies in order of the likelihood
that each comprises UXO, our LGP tool was able to
substantially improve the discrimination of UXO from
scrap-preexisting techniques require digging 62% more
holes to locate all UXO on a range than do LGP derived
models; (2) LGP can improve discrimination even though
trained on a very small number of examples of UXO; and
(3) LGP can improve UXO discrimination on data sets
that contain a high-level of noise and little
preprocessing.
%0 Conference Paper
%1 francone:2004:lbp
%A Francone, Frank D.
%A Deschaine, Larry M.
%A Battenhouse, Tom
%A Warren, Jeffrey J.
%B Late Breaking Papers at the 2004 Genetic and
Evolutionary Computation Conference
%C Seattle, Washington, USA
%D 2004
%E Keijzer, Maarten
%K algorithms, genetic programming
%T Discrimination of Unexploded Ordnance from Clutter
Using Linear Genetic Programming
%U http://www.aimlearning.com/UXO.GECCO.2004.pdf
%X We used Linear Genetic Programming (LGP) to study the
extent to which automated learning techniques may be
used to improve Unexploded Ordinance (UXO)
discrimination from Protem-47 and Geonics EM61
non-invasive electromagnetic sensors. We conclude that:
(1) Even after geophysicists have analysed the EM61
signals and ranked anomalies in order of the likelihood
that each comprises UXO, our LGP tool was able to
substantially improve the discrimination of UXO from
scrap-preexisting techniques require digging 62% more
holes to locate all UXO on a range than do LGP derived
models; (2) LGP can improve discrimination even though
trained on a very small number of examples of UXO; and
(3) LGP can improve UXO discrimination on data sets
that contain a high-level of noise and little
preprocessing.
@inproceedings{francone:2004:lbp,
abstract = {We used Linear Genetic Programming (LGP) to study the
extent to which automated learning techniques may be
used to improve Unexploded Ordinance (UXO)
discrimination from Protem-47 and Geonics EM61
non-invasive electromagnetic sensors. We conclude that:
(1) Even after geophysicists have analysed the EM61
signals and ranked anomalies in order of the likelihood
that each comprises UXO, our LGP tool was able to
substantially improve the discrimination of UXO from
scrap-preexisting techniques require digging 62% more
holes to locate all UXO on a range than do LGP derived
models; (2) LGP can improve discrimination even though
trained on a very small number of examples of UXO; and
(3) LGP can improve UXO discrimination on data sets
that contain a high-level of noise and little
preprocessing.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Seattle, Washington, USA},
author = {Francone, Frank D. and Deschaine, Larry M. and Battenhouse, Tom and Warren, Jeffrey J.},
biburl = {https://www.bibsonomy.org/bibtex/24445ac174bed50a059ccd58f46b7c6ba/brazovayeye},
booktitle = {Late Breaking Papers at the 2004 Genetic and
Evolutionary Computation Conference},
editor = {Keijzer, Maarten},
interhash = {41dc2d863ecace6f12d5fa08608c1caa},
intrahash = {4445ac174bed50a059ccd58f46b7c6ba},
keywords = {algorithms, genetic programming},
month = {26 July},
notes = {Part of \cite{keijzer:2004:GECCO:lbp}. See also
\cite{francone:2005:GPTP}.},
timestamp = {2008-06-19T17:39:46.000+0200},
title = {Discrimination of Unexploded Ordnance from Clutter
Using Linear Genetic Programming},
url = {http://www.aimlearning.com/UXO.GECCO.2004.pdf},
year = 2004
}