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Evolutionary strategy for learning multiple-valued logic functions

We consider the problem of synthesizing multiple-valued logic functions by neural networks. An evolutionary strategy (ES) which finds the longest strip in V/spl sube/K/sup n/ is described. A strip contains points located between two parallel hyperplanes. Repeated application of ES partitions the space V into a certain number of strips, each of them corresponding to a hidden unit. We construct neural networks based on these hidden units. Preliminary experimental results are presented and discussed.