Abstract
The existence of a block design with a. given set of parameters is a well-known problem from combinatorial theory. Although there exist some constructive methods, the generation of block designs in the general case is an NP-complete problem.
In this paper we use optimizing neural networks as an heuristic approach to the generation of block designs. First, a cost function is defined and mapped onto a network which has connections of arity four. This network is then used for the generation of some designs that are known to exist. For some designs the results are good, but for some others the system fails to find an optimal solution in a reasonable time. The problem is shown to be a good example to test the performance of optimizing neural networks.
This work has been partially supported by the project CICYT TIC-91-0423
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Bofill, P., Torras, C. (1993). Higher-order networks for the optimization of block designs. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_133
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DOI: https://doi.org/10.1007/3-540-56798-4_133
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