Multiprocessor Document Allocation: a Neural Network Approach

We consider the problem of distributing the documents to a given set of processors so that the load on each processor is as equal as possible and the amount of communication is as small as possible. This is an NP-Complete problem. We apply continuous as well as discrete Hop:field neural networks to obtain suboptimal solutions for the problem. These networks perform better than a genetic algorithm for this task proposed by Frieder et al. [4]; in particular, the continuous Hopfield network performs extremely well.

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