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Optimization of very expensive black-box functions requires utilization ofmaximum information gathered by the process of optimization. Model GuidedSampling Optimization (MGSO) forms a more robust alternative to Jones'Gaussian-process-based EGO algorithm. Instead of EGO's maximizing expectedimprovement, the MGSO uses sampling the probability of improvement which isshown to be helpful against trapping in local minima. Further, the MGSO canreach close-to-optimum solutions faster than standard optimization algorithmson low dimensional or smooth problems.

Model Guided Sampling Optimization for Low-dimensional Problems