Evolving Networks: Using the Genetic Algorithm with Connectionist Learning

R. K. Belew, J. McInerney, and N. N. Schraudolph. Evolving Networks: Using the Genetic Algorithm with Connectionist Learning. In C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, editors, Artificial Life II, SFI Studies in the Sciences of Complexity: Proceedings, pp. 511–547, Addison-Wesley, Redwood City, CA, 1992.

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Abstract

It is appealing to consider hybrids of neural network learnning algorithms with evolutionary search procedures simply because nature has successfully done so, suggesting that such hybrids may be more efficient than either technique applied in isolation. We survey recent work in this area and report our own experiments on using the GA to search the space of initial conditions for backpropagation networks. We find that use of the GA provides much greater confidence in the face of the dependence on initial conditions that plague gradient techniques, and allows a reduction of individual training time by as much as two orders of magnitude. We conclude that the GA's global sampling characteristics complement connectionist local search techniques well, leading to efficient and robust hybrids.

BibTeX Entry

@incollection{BelMcISch92,
author = {Richard K. Belew and John McInerney and Nicol N. Schraudolph},
title = {\href{http://nic.schraudolph.org/pubs/BelMcISch92.pdf}{
Evolving Networks: Using the Genetic Algorithm
with Connectionist Learning}},
editor = {Christopher G. Langton and Charles Taylor and J. Doyne Farmer
and Steen Rasmussen},
booktitle = {Artificial Life II},
series = {SFI Studies in the Sciences of Complexity: Proceedings},
volume =  10,
pages = {511--547},
year =  1992,
b2h_type = {Book Chapters},
b2h_topic = {Evolutionary Algorithms},
abstract = {
It is appealing to consider hybrids of neural network learnning algorithms
with evolutionary search procedures simply because nature has successfully
done so, suggesting that such hybrids may be more efficient than either
technique applied in isolation.  We survey recent work in this area and
report our own experiments on using the GA to search the space of initial
conditions for backpropagation networks.
We find that use of the GA provides much greater confidence in the face of
the dependence on initial conditions that plague gradient techniques, and
allows a reduction of individual training time by as much as two orders of
magnitude.  We conclude that the GA's {\em global sampling}
characteristics complement connectionist {\em local search}
techniques well, leading to efficient and robust hybrids.
}}


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