Genetic Programming Approach for Fault Modeling of Electronic Hardware

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{abraham:2005:CEC,

• author = "Ajith Abraham and Crina Grosan",
• title = "Genetic Programming Approach for Fault Modeling of Electronic Hardware",
• booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation",
• year = "2005",
• 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",
• volume = "2",
• pages = "1563--1569",
• address = "Edinburgh, UK",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• month = "2-5 " # sep,
• organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)",
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, MEP, ANN, LGP",
• ISBN = "0-7803-9363-5",
• URL = "http://www.softcomputing.net/cec05.pdf",
• DOI = "doi:10.1109/CEC.2005.1554875",
• size = "7 pages",
• 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.",
• notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.",

}

Genetic Programming entries for Ajith Abraham Crina Grosan

Citations