  Optimization of very expensive black-box functions requires utilization of
maximum information gathered by the process of optimization. Model Guided
Sampling Optimization (MGSO) forms a more robust alternative to Jones'
Gaussian-process-based EGO algorithm. Instead of EGO's maximizing expected
improvement, the MGSO uses sampling the probability of improvement which is
shown to be helpful against trapping in local minima. Further, the MGSO can
reach close-to-optimum solutions faster than standard optimization algorithms
on low dimensional or smooth problems.
