Genetic Programming Approach for Fault Modeling of Electronic Hardware

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  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
  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,
  ISBN =         "0-7803-9363-5",
  URL =          "",
  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