Abstract
Some classes of neural networks are known as universal function approximators. However, their training efficiency and generalization performance depend highly on the structure which is usually determined by a human designer. In this paper we present an evolutionary computation method for automating the neural network design process. We represent networks as tree structures, called neural trees, in genotype and apply genetic operators to evolve problem-dependent network structures and their weights. Experimental results are provided on the prediction of a heart rate time-series by evolving sigma-pi neural trees.
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© 1998 Springer-Verlag London
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Zhang, BT., Joung, JG. (1998). Evolutionary Design of Neural Trees for Heart Rate Prediction. In: Chawdhry, P.K., Roy, R., Pant, R.K. (eds) Soft Computing in Engineering Design and Manufacturing. Springer, London. https://doi.org/10.1007/978-1-4471-0427-8_11
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DOI: https://doi.org/10.1007/978-1-4471-0427-8_11
Publisher Name: Springer, London
Print ISBN: 978-3-540-76214-0
Online ISBN: 978-1-4471-0427-8
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