(Enter summary)
Abstract: this paper is to present an optimization algorithm, comprising a multiobjective
evolutionary algorithm and a gradient--based local search. In the rest
of the paper, this is referred to as memetic Pareto artificial neural network
(MPANN) algorithm for training ANNs. The evolutionary approach is used
to simultaneously train the network and optimize its architecture. The result
is a set of networks, with each network in the set attempts to optimize both
the training error and the architecture. We... (Update)
Cited by: More
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BibTeX entry: (Update)
Hussein A. Abbass. Speeding up back-propagation using multiobjective evolutionary algorithms. Neural Computation (Accepted to appear), 2003. http://citeseer.ist.psu.edu/abbass03speeding.html More
@misc{ abbass03speeding,
author = "H. Abbass",
title = "Speeding up back-propagation using multiobjective evolutionary algorithms",
text = "Hussein A. Abbass. Speeding up back-propagation using multiobjective evolutionary
algorithms. Neural Computation (Accepted to appear), 2003.",
year = "2003",
url = "citeseer.ist.psu.edu/abbass03speeding.html" }
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