Stochastic Radial Basis Function Algorithm for Global Optimization

Solves computationally expensive black-box global optimization problems with box constraints

The Stochastic Radial Basis Function Algorithm aims at solving computationally expensive continuous black-box global optimization problems with box constraints. The algorithm uses radial basis functions to approximate the true objective function and to decide at which points in the variable domain the costly objective function should be evaluated. The algorithm uses a scoring criterion to select sample points, hence no auxiliary problem needs to be solved. The algorithm can do more than one function evaluation in parallel in each iteration if desired.