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Proceedings Paper

Training neural networks with genetic algorithms for target detection
Author(s): Alan V. Scherf; Lawrence D. Voelz
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Paper Abstract

Algorithms for training artificial neural networks, such as backpropagation, often employ some form of gradient descent in their search for an optimal weight set. The problem with such algorithms is their tendency to converge to local minima, or not to converge at all. Genetic algorithms simulate evolutionary operators in their search for optimality. The techniques of genetic search are applied to training a neural network for target detection in infrared imagery. The algorithm design, parameters, and experimental results are detailed. Testing verifies that genetic algorithms are a useful and effective approach for neural network training.

Paper Details

Date Published: 1 July 1992
PDF: 8 pages
Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140134
Show Author Affiliations
Alan V. Scherf, Loral Aeronutronic (United States)
Lawrence D. Voelz, Loral Aeronutronic (United States)

Published in SPIE Proceedings Vol. 1710:
Science of Artificial Neural Networks
Dennis W. Ruck, Editor(s)

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