30 June 1994 Evolutionary optimization of cascaded networks
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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.
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John R. McDonnell, John R. McDonnell, Donald E. Waagen, Donald E. Waagen, } "Evolutionary optimization of cascaded networks", Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); doi: 10.1117/12.179233; https://doi.org/10.1117/12.179233
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