Association-Based Evolution of Comprehensible Neural Logic Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{chia:2004:lbp,

• 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 = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP061.pdf",
• 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 paradigm.",
• notes = "Part of \cite{keijzer:2004:GECCO:lbp}",

}

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

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