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Proceedings Paper

Hopfield networks and scheduling problems
Author(s): William J. Wolfe
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Paper Abstract

In this paper we present a neural generator method that uses a neural network to generate initial search points for a discrete heuristic. We demonstrate the method for the subset-sum problem (SSP), and consider the SSP to be typical of the sub-problems that a scheduling algorithm must solve while on route to solving an entire scheduling problem. The neural generator method hinges on using the continuous valued activations of the neural system to select a corner of the n-cube that can be used to initialize a discrete search. This can be done at each neural iteration, resulting in many discrete searches over the source of a single neural run. Without the discrete heuristic, the selected corners can be interpreted as instantaneous neural solutions and the best-so-far tabulated as the neural system runs. This allows the neural system to be terminated without losing the full effort of the run, and should the network be run until convergence, the best-so-far result is at least as good as the convergent corner, and usually better. With the discrete heuristic, a search is launched from the instantaneous neural solutions, greatly improving the overall results (again keeping the best-so-far). The results are presented for an n equals 25 SSP.

Paper Details

Date Published: 6 April 1995
PDF: 8 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205138
Show Author Affiliations
William J. Wolfe, Univ. of Colorado/Denver (United States)

Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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