Many practical problems require the calculation of an optimum (global) according to a general program P. However the model on which the optimal is based may be incomplete in the sense that important uncertainties have not been considered. In order to evaluate the effects of the uncertainty of the parameters, the decision-maker needs to evaluate the range of variation of program P. In this work a two-step evolutionary approach to analyze uncertainties in optimization programs is presented. The proposed approach combines the two proven techniques of Cellular Evolutionary Strategies (CES) and Evolutionary Strategies (ES). © Springer-Verlag 2000.
CITATION STYLE
Rocco S., C. M., Miller, A. J., Moreno, J. A., Carrasquero, N., & Medina, M. (2000). Sensitivity and uncertainty analysis in optimization programs using an evolutionary approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1952 LNAI, pp. 487–496). https://doi.org/10.1007/3-540-44399-1_50
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