Neural Network Weight Space Symmetries Can Speed up Genetic Learning
نویسنده
چکیده
A functional equivalence of feed-forward networks has been proposed to reduce the search space of learning algorithms. A novel genetic learning algorithm for RBF networks and perceptrons with one hidden layer that makes use of this theoretical property is proposed. Experimental results show that our procedure outperforms the standard genetic learning. Key-Words: Feedforward neural networks, genetic learning algoritms.
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