Genomic computing networks learn complex POMDPs
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Grammar based crossover operator in genetic programming
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
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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.