Attribute Grammar Encoding of the Structure and Behaviour of Artificial Neural Networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @PhdThesis{Talib.Hussain:thesis,
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author = "Talib Sajad Hussain",
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title = "Attribute Grammar Encoding of the Structure and
Behaviour of Artificial Neural Networks",
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school = "School of Computing, Queen's University",
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year = "2003",
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address = "Kinston, Ontario, Canada",
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month = aug,
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keywords = "genetic algorithms, genetic programming, ANN",
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URL = "https://drive.google.com/file/d/1OGQbBSfLF2IiCuBP62ra6Z_aJQpLTHls/view?pli=1",
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size = "403 pages",
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abstract = "Current techniques for the abstract representation of
complex artificial neural network architectures are
limited in the variety and types of neural network
characteristics that may be represented. The Network
Generated Attribute Grammar Encoding (NGAGE) technique
is introduced to address these limitations. NGAGE uses
an attribute grammar to explicitly represent both
topological and behavioural properties of a neural
network, and uses a common neural interpreter to
generate functional neural networks from a derivation
of the grammar. Grammars that represent a wide variety
of current and novel neural network architectures are
presented. Together, these grammars demonstrate that
the NGAGE technique has greater representation
flexibility than current approaches. A novel
evolutionary algorithm, the Probabilistic Context-Free
Grammar Genetic Programming (PCFG-GP), is introduced to
enable a constrained evolutionary search of the space
of context-free parse trees generated by an attribute
grammar. Experimental results demonstrating the search
behaviour of the PCFG-GP algorithm are presented. The
NGAGE technique is shown to be a valuable tool for the
representation and exploration of novel and existing
neural network architectures.",
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notes = "Supervisor: Roger A. Browse",
- }
Genetic Programming entries for
Talib S Hussain
Citations