Learning the neuron functions within a neural network via genetic programming: applications to geophysics and hydrogeology

Authors:
Alan J. Barton;Julio J. Valdés;Robert Orchard
Affiliations:
Institute for Information Technology, National Research Council Canada, Ottawa, Ontario, Canada;Institute for Information Technology, National Research Council Canada, Ottawa, Ontario, Canada;Institute for Information Technology, National Research Council Canada, Ottawa, Ontario, Canada
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 6
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Neural Networks

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Abstract

A neural network classifier is sought. Classical neural network neurons are aggregations of a weight multiplied by an input value and then controlled via an activation function. This paper learns everything within the neuron using a variant of Genetic Programming called Gene Expression Programming. That is, this paper does not explicitly use weights or activation functions within a neuron, nor bias nodes within a layer. Promising preliminary results are reported for a study of the detection of underground caves (a 1 class problem) and for a study of the interaction of water and minerals near a glacier in the Arctic (a 5 class problem).