Abstract
Most neural networks require a set of training examples in order to attempt to approximate a problem function. For many real-world problems, however, such a set of examples is unavailable. Such a problem involving feedback optimization of a computer network routing system has motivated a general method of generating artificial training sets using evolutionary computation. This paper describes the method and demonstrates its utility by presenting promising results from applying it to an artificial problem similar to a real-world network routing optimization problem.
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