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Proceedings Paper

Evolutionary optimization of cascaded networks
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Paper Abstract

This work investigates the application of evolutionary search to cascade-correlation learning architectures. Evolutionary programming is used to generate the hidden weights of each candidate hidden unit in the cascade-correlation learning paradigm. The output weights are adapted using deterministic techniques. Evolutionary search is also used to modify the connectivity of each candidate unit so that parsimonious structures may be generated during the neural network construction process. This approach is appealing from a computational perspective since only a population of hidden nodes is being optimized as opposed to a population of neural networks. Results are given for selected low-dimensional examples.

Paper Details

Date Published: 30 June 1994
PDF: 12 pages
Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); doi: 10.1117/12.179233
Show Author Affiliations
John R. McDonnell, Naval Command, Control and Ocean Surveillance Ctr. (United States)
Donald E. Waagen, TRW, Inc. (United States)


Published in SPIE Proceedings Vol. 2304:
Neural and Stochastic Methods in Image and Signal Processing III
Su-Shing Chen, Editor(s)

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