Clustered multiple generalized expected improvement: A novel infill sampling criterion for surrogate models

Surrogate model-based optimization is a well-known technique for optimizing expensive black-box functions. By applying this function approximation, the number of real problem evaluations can be reduced because the optimization is performed on the model. In this case two contradictory targets have to be achieved: increasing global model accuracy and exploiting potentially optimal areas. The key to these targets is the criterion for selecting the next point, which is then evaluated on the expensive black-box function - the dasiainfill sampling criterionpsila. Therefore, a novel approach - the dasiaClustered Multiple Generalized Expected Improvementpsila (CMGEI) - is introduced and motivated by an empirical study. Furthermore, experiments benchmarking its performance compared to the state of the art are presented.