Association-Based Evolution of Comprehensible Neural Logic Networks

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

  author =       "Henry Wai-Kit Chia and Chew-Lim Tan",
  title =        "Association-Based Evolution of Comprehensible Neural
                 Logic Networks",
  booktitle =    "Late Breaking Papers at the 2004 Genetic and
                 Evolutionary Computation Conference",
  year =         "2004",
  editor =       "Maarten Keijzer",
  address =      "Seattle, Washington, USA",
  month =        "26 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  abstract =     "Neural Logic Network (Neulonet) learning has been
                 successfully used in emulating complex human reasoning
                 processes. One recent implementation generates a single
                 large neulonet via genetic programming using an
                 accuracy-based fitness measure. However, in terms of
                 human comprehensibility and amenability during logic
                 inference, evolving multiple compact neulonets are
                 preferred. The present work realizes this by adopting
                 associative-classification measures of confidence and
                 support as part of the fitness computation. The evolved
                 neulonets are combined together to form an eventual
                 macro-classier. Empirical study shows that associative
                 classification integrated with neulonet learning
                 performs better than general association-based
                 classifiers in terms of higher accuracies and smaller
                 rule sets. This is primarily due to the richness in
                 logic expression inherent in the neulonet learning
  notes =        "Part of \cite{keijzer:2004:GECCO:lbp}",

Genetic Programming entries for Henry Wai-Kit Chia Chew-Lim Tan