Weighted ensembles in model-based global optimization

It is a common technique in global optimization with expensive black-box functions, to learn a regression model (or surrogate-model) of the response function from past evaluations and to use this model to decide on the location of future evaluations. In surrogate model assisted optimization it can be difficult to select the right modeling technique. Without preliminary knowledge about the function it might be beneficial if the algorithm trains as many different surrogate models as possible and selects the model with the smallest training error. This is known as model selection. Recently a generalization of this approach was proposed: instead of selecting a single model we propose to use optimal convex combinations of model predictions. This approach, called model mixtures, is adopted and evaluated in the context of sequential parameter optimization. Besides discussing the general strategy, the optimal frequency of learning the convex weights is investigated. The feasibility of this approach is examined and its benefits are compared to simpler methods.