Attribute Grammar Encoding of the Structure and Behaviour of Artificial Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@PhdThesis{Talib.Hussain:thesis,

• author = "Talib Sajad Hussain",
• title = "Attribute Grammar Encoding of the Structure and Behaviour of Artificial Neural Networks",
• school = "School of Computing, Queen's University",
• year = "2003",
• address = "Kinston, Ontario, Canada",
• month = aug,
• keywords = "genetic algorithms, genetic programming, ANN",
• URL = "https://drive.google.com/file/d/1OGQbBSfLF2IiCuBP62ra6Z_aJQpLTHls/view?pli=1",
• size = "403 pages",
• 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.",
• notes = "Supervisor: Roger A. Browse",

}

Genetic Programming entries for Talib S Hussain

Citations