NDLI: Evolutionary programming for training neural networks

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International 1989 Joint Conference on Neural Networks

Evolutionary programming for training neural networks

Content Provider	IEEE Xplore Digital Library
Author	Fogel, D.B. Fogel, L.J. Porto, V.W.
Copyright Year	1990
Abstract	The use of evolutionary programming to train a neural network is addressed by examining a simple classification problem involving two populations of interest. The first follows a Gaussian distribution having zero means and unit variance; the second is also Gaussian, but with a mean of one and unit variance. Ten independent samples are drawn from either one of the populations with the objective being to properly identify the appropriate underlying distribution. A network consisting of 10 input nodes, a single hidden layer of 10 nodes, and a final classifying node was constructed. To train the network, evolutionary programming was used to minimize the sum of the squared differences between each target output and the actual network output. A population of 10 parent vectors representing the 121 weights and biases of the network was maintained, and the limit of evolution was chosen to be 1000 generations. A training set of 100 patterns, 50 from each population, was determined randomly and then fixed for the extent of the evolution. The results indicate that evolutionary programming is useful for discovering the optimal set of weights in a neural network
Starting Page	601
Ending Page	605
File Size	313654
Page Count	5
File Format	PDF
DOI	10.1109/IJCNN.1990.137636
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1990-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article

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