Neural Logic Network Learning Using Genetic Programming
Created by W.Langdon from
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- @Article{DBLP:journals/ijcia/ChiaT01,
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author = "Henry Wai Kit Chia and Chew Lim Tan",
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title = "Neural Logic Network Learning Using Genetic
Programming",
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journal = "International Journal of Computational Intelligence
and Applications",
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volume = "1",
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number = "4",
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year = "2001",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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pages = "357--368",
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keywords = "genetic algorithms, genetic programming, Neural
network, rule-based learning, data mining",
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DOI = "doi:10.1142/S1469026801000299",
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abstract = "Neural Logic Networks or Neulonets are hybrids of
neural networks and expert systems capable of
representing complex human logic in decision making.
Each neulonet is composed of rudimentary net rules
which themselves depict a wide variety of fundamental
human logic rules. An early methodology employed in
neulonet learning for pattern classification involved
weight adjustments during back-propagation training
which ultimately rendered the net rules
incomprehensible. A new technique is now developed that
allows the neulonet to learn by composing the net rules
using genetic programming without the need to impose
weight modifications, thereby maintaining the inherent
logic of the net rules. Experimental results are
presented to illustrate this new and exciting
capability in capturing human decision logic from
examples. The extraction and analysis of human logic
net rules from an evolved neulonet will be discussed.
These extracted net rules will be shown to provide an
alternate perspective to the greater extent of
knowledge that can be expressed and discovered.
Comparisons will also be made to demonstrate the added
advantage of using net rules, against the use of
standard boolean logic of negation, disjunction and
conjunction, in the realm of evolutionary
computation.",
- }
Genetic Programming entries for
Henry Wai-Kit Chia
Chew-Lim Tan
Citations