Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Frank D. Francone"'
Autor:
Frank D. Francone, Larry M. Deschaine
Publikováno v:
Information Sciences. 161:99-120
Optimized models of complex physical systems are difficult to create and time consuming to optimize. The physical and business processes are often not well understood and are therefore difficult to model. The models of often too complex to be well op
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07dec643572734cf81a03f06b91e3a8d
https://doi.org/10.1016/j.ins.2003.05.006
https://doi.org/10.1016/j.ins.2003.05.006
The demonstration described in this report was conducted at the Former Camp Sibert, Alabama, under project ESTCP MM-0811 LGP Discrimination and Residual Risk Analysis at Camp Sibert. It was performed under the umbrella of the ESTCP Discrimination Stu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bb400bf17f2fda0409288c76ffebc098
https://doi.org/10.21236/ada603044
https://doi.org/10.21236/ada603044
Publikováno v:
GECCO
Removing underground, unexploded bombs, mortars, cannon-shells and other ordnance ("MEC" or "UXO") from former military ranges is difficult and expensive. The principal difficulty is discriminating intact, underground ordnance from other metallic ite
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::70a83e08c7e2f1275a1d82990ce6a8a3
https://doi.org/10.1145/1276958.1277353
https://doi.org/10.1145/1276958.1277353
Publikováno v:
Genetic Programming Theory and Practice III ISBN: 038728110X
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 tha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::564fd56d0125092cd40bf54732c1a8d3
https://doi.org/10.1007/0-387-28111-8_4
https://doi.org/10.1007/0-387-28111-8_4
Publikováno v:
Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).
Many machine learning tasks are just too hard to be solved with a single processor machine, no matter how efficient the algorithms are and how fast our hardware is. Luckily genetic programming is well suited for parallelization compared to standard s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2bc65933a324cacfdf162163d482f9f3
https://doi.org/10.1109/cec.1999.782540
https://doi.org/10.1109/cec.1999.782540
Publikováno v:
Parallel Problem Solving from Nature — PPSN IV ISBN: 9783540617235
PPSN
PPSN
Ordinarily, Genetic Programming uses little or no mutation. Crossover is the predominant operator. This study tests the effect of a very aggressive use of the mutation operator on the generalization performance of our Compiling Genetic Programming Sy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c3555089a7f1fae051c1179c8aa15964
https://doi.org/10.1007/3-540-61723-x_994
https://doi.org/10.1007/3-540-61723-x_994