DACE - A Matlab Kriging Toolbox

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Kriging metamodeling in simulation: A review (2009, methodology): “) The estimation of the correlation functions and the corresponding optimal weights is made easy through DACE, which is well documented and free of charge; see [35].”
A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization (2018, methodology): “In this paper, we use a modification of Hookes and Jeeves algorithm [21], which is implemented in DACE toolbox [30].”
Sequential parameter optimization (2005, unknown): “[6] and extends heuristics (e.”
The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics (2013, methodology): “The implementation and the use of the Kriging model rely on the DACE Matlab toolbox [36].”
Calculations of Sobol indices for the Gaussian process metamodel (2008, methodology): “[14]) uses a powerful stochastic algorithm based on the Hooke & Jeeves method (Bazaraa et al.”
Reliability-based design optimization using kriging surrogates and subset simulation (2011, methodology): “…can show that β and σ 2 Y can be derived analytically (using the ﬁrst-order optimality conditions) and solely depends on the autocovariance parameters ℓ that are solution of a numerically tractable global optimization problem – see e.g. Welch et al. (1992); Lophaven et al. (2002) for more details.”