Abstract
Global optimization in electrical engineering usually requires an enormous amount of CPU time to evaluate the objective function when stochastic methods are used. Approximating the objective function can drastically reduce the computational demands. The use of feedforward neural networks is proposed in this paper and its application is investigated using an unconstrained and a constrained version of the TEAM Workshop problem 22.
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