%0 Book Section %1 francone:2005:GPTP %A Francone, Frank D. %A Deschaine, Larry M. %A Battenhouse, Tom %A Warren, Jeffrey J. %B Genetic Programming Theory and Practice III %C Ann Arbor %D 2005 %E Yu, Tina %E Riolo, Rick L. %E Worzel, Bill %I Springer %K Discrimination. Ordnance, UXO Unexploded algorithms, genetic programming, %P 49--64 %T Discrimination of Unexploded Ordnance from Clutter using Linear Genetic Programming %V 9 %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 62percent 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. %& 4 %@ 0-387-28110-X

@incollection{francone:2005:GPTP, 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 62percent 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 = {Ann Arbor}, author = {Francone, Frank D. and Deschaine, Larry M. and Battenhouse, Tom and Warren, Jeffrey J.}, biburl = {https://www.bibsonomy.org/bibtex/269e08a3fd904a6bce6251b58d3a6697e/brazovayeye}, booktitle = {Genetic Programming Theory and Practice {III}}, chapter = 4, editor = {Yu, Tina and Riolo, Rick L. and Worzel, Bill}, interhash = {d1a7efe91da9ed45891aff50d31bef3d}, intrahash = {69e08a3fd904a6bce6251b58d3a6697e}, isbn = {0-387-28110-X}, keywords = {Discrimination. Ordnance, UXO Unexploded algorithms, genetic programming,}, month = {12-14 May}, notes = {part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop}, pages = {49--64}, publisher = {Springer}, series = {Genetic Programming}, size = {16 pages}, timestamp = {2008-06-19T17:39:46.000+0200}, title = {Discrimination of Unexploded Ordnance from Clutter using Linear Genetic Programming}, volume = 9, year = 2005 }