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Summary: 1
Hopfield Neural Networks for Timetabling: Formulations, Methods,
and Comparative Results
Kate A. Smith1*
, David Abramson2
, and David Duke2
1
School of Business Systems, and
2
School of Computer Science and Software Engineering
P. O. Box 63B, Monash University
Victoria 3800
Australia
Abstract
This paper considers the use of discrete Hopfield neural networks for solving school
timetabling problems. Two alternative formulations are provided for the problem: a
standard Hopfield-Tank approach, and a more compact formulation which allows the
Hopfield network to be competitive with swapping heuristics. It is demonstrated how
these formulations can lead to different results. The Hopfield network dynamics are
also modified to allow it to be competitive with other metaheuristics by incorporating
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