Classification-based Optimization with Multi-Fidelity Evaluations

Classification-based optimization (CBO) is a recently proposed global optimization method which exhibits a good prospect. Previous studies have shown that CBO has a good performance when a function evaluation is cheap. However, its performance is far from being completely and precisely consummated in the optimization of an expensive black box function f. We term this task as multi-fidelity black-box optimization and develop MF-CBO, a novel method based on CBO. To the best of our knowledge, this is the first work to extend classification-based optimization methods to the multi-fidelity case. MF-CBO explores the space using the low fidelities and exploits the high fidelity on successively smaller regions. Experimental results demonstrate that MF-CBO outperforms the strategies which ignore the multi-fidelity information and other multi-fidelity methods on several real cases and synthetic examples.