Abstract
The reduction of computational cost of evolutionary multiobjective Pareto-optimization algorithms is necessary when time-consuming objective functions evaluation is required. To this end neural network interpolation techniques are used for objective response surface building; evolutionary multiobjective Pareto-optimization is then performed on interpolated functions. Both analytical multiobjective test problems and numerical electromagnetic design problems are considered; the study of Pareto optimal front interpolation accuracy versus neural network training cost is performed.
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