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GECCO
2004
Springer

A Descriptive Encoding Language for Evolving Modular Neural Networks

12 years 8 months ago
A Descriptive Encoding Language for Evolving Modular Neural Networks
Evolutionary algorithms are a promising approach for the automated design of artificial neural networks, but they require a compact and efficient genetic encoding scheme to represent repetitive and recurrent modules in networks. Here we introduce a problem-independent approach based on a human-readable descriptive encoding using a highlevel language. We show that this approach is useful in designing hierarchical structures and modular neural networks, and can be used to describe the search space as well as the final resultant networks.
Jae-Yoon Jung, James A. Reggia
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where GECCO
Authors Jae-Yoon Jung, James A. Reggia
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