
doi: 10.2139/ssrn.930401
Generalized Response Surface Methodology (GRSM) is a novel general-purpose metaheuristic based on Box and Wilson.s Response Surface Methodology (RSM).Both GRSM and RSM estimate local gradients to search for the optimal solution.These gradients use local first-order polynomials.GRSM, however, uses these gradients to estimate a better search direction than the steepest ascent direction used by RSM.Moreover, GRSM allows multiple responses, selecting one response as goal and the other responses as constrained variables.Finally, these estimated gradients may be used to test whether the estimated solution is indeed optimal.The focus of this paper is optimization of simulated systems.
experimental design;multivariate regression analysis;least squares;Karush-Kuhn-Tucker conditions;bootstrap, experimental design; multivariate regression analysis; least squares; Karush-Kuhn-Tucker conditions; bootstrap, experimental design, least squares, multivariate regression analysis, bootstrap, Karush-Kuhn-Tucker conditions, jel: jel:C9, jel: jel:C0, jel: jel:C1
experimental design;multivariate regression analysis;least squares;Karush-Kuhn-Tucker conditions;bootstrap, experimental design; multivariate regression analysis; least squares; Karush-Kuhn-Tucker conditions; bootstrap, experimental design, least squares, multivariate regression analysis, bootstrap, Karush-Kuhn-Tucker conditions, jel: jel:C9, jel: jel:C0, jel: jel:C1
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