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This work proposes a sequential optimization algorithm, EORKS, combining a Kriging surrogate from an adaptive sampling and an iterative constrained search in the dynamic reliable regions to reduce the sampling size in expensive optimization. A surrogate established from small samples is liable to limited generality, which leads to a false prediction of optimum. EORKS applies Kriging variance to establish the reliable region neighbouring the learning samples to constrain the evolutionarydoi:10.6084/m9.figshare.7931531.v1 fatcat:vpzvs6uzivalvihjadewxbdoue