Genetic Programming Approach for Fault Modeling of Electronic Hardware
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{abraham:2005:CEC,
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author = "Ajith Abraham and Crina Grosan",
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title = "Genetic Programming Approach for Fault Modeling of
Electronic Hardware",
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booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
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year = "2005",
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editor = "David Corne and Zbigniew Michalewicz and
Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and
Garrison Greenwood and Tan Kay Chen and
Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and
Jennifier Willies and Juan J. Merelo Guervos and
Eugene Eberbach and Bob McKay and Alastair Channon and
Ashutosh Tiwari and L. Gwenn Volkert and
Dan Ashlock and Marc Schoenauer",
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volume = "2",
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pages = "1563--1569",
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address = "Edinburgh, UK",
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publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
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month = "2-5 " # sep,
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organisation = "IEEE Computational Intelligence Society, Institution
of Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming, MEP, ANN,
LGP",
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ISBN = "0-7803-9363-5",
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URL = "http://www.softcomputing.net/cec05.pdf",
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DOI = "doi:10.1109/CEC.2005.1554875",
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size = "7 pages",
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abstract = "presents two variants of Genetic Programming (GP)
approaches for intelligent online performance
monitoring of electronic circuits and systems.
Reliability modelling of electronic circuits can be
best performed by the stressor - susceptibility
interaction model. A circuit or a system is deemed to
be failed once the stressor has exceeded the
susceptibility limits. For on-line prediction,
validated stressor vectors may be obtained by direct
measurements or sensors, which after preprocessing and
standardisation are fed into the GP models. Empirical
results are compared with artificial neural networks
trained using backpropagation algorithm. The
performance of the proposed method is evaluated by
comparing the experiment results with the actual
failure model values. The developed model reveals that
GP could play an important role for future fault
monitoring systems.",
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notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and
the EPS.",
- }
Genetic Programming entries for
Ajith Abraham
Crina Grosan
Citations