Fault classification using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Liang Zhang and Asoke K. Nandi",
  title =        "Fault classification using genetic programming",
  journal =      "Mechanical Systems and Signal Processing",
  year =         "2007",
  volume =       "21",
  number =       "3",
  pages =        "1273--1284",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, Condition
                 monitoring, Multi-class classification, Fault
                 classification, Roller bearing",
  DOI =          "doi:10.1016/j.ymssp.2006.04.004",
  abstract =     "Genetic programming (GP) is a stochastic process for
                 automatically generating computer programs. In this
                 paper, three GP-based approaches for solving
                 multi-class classification problems in roller bearing
                 fault detection are proposed. Single-GP maps all the
                 classes onto the one-dimensional GP output.
                 Independent-GPs singles out each class separately by
                 evolving a binary GP for each class independently.
                 Bundled-GPs also has one binary GP for each class, but
                 these GPs are evolved together with the aim of
                 selecting as few features as possible. The
                 classification results and the features each algorithm
                 has selected are compared with genetic algorithm (GA)
                 based approaches GA/ANN and GA/SVM. Experiments show
                 that bundled-GPs is strong in feature selection while
                 retaining high performance, which equals or outperforms
                 the two previous GA-based approaches.",

Genetic Programming entries for Liang Zhang Asoke K Nandi