A genetic algorithm is used to generate binary reference functions for optical pattern recognition and classification. Procedures based on the properties of convex functions can be implemented directly on hybrid electro-optical systems. Computer simulations demonstrate the efficiency of this novel approach.

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Properties of the entropy function encountered in physics and information theory are employed in the generation of highly selective spatial filters for pattern recognition. Computer simulations and laboratory experiments demonstrate efficient recognition of single patterns or classes even when these are submerged in high level random noise.

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