Surrogate Assisted Feature Computation for Continuous Problems

A possible approach to Algorithm Selection and Configuration for continuous black box optimization problems relies on problem features, computed from a set of evaluated sample points. However, the computation of these features requires a rather large number of such samples, unlikely to be practical for expensive real-world problems. On the other hand, surrogate models have been proposed to tackle the optimization of expensive objective functions. This paper proposes to use surrogate models to approximate the values of the features at reasonable computational cost. Two experimental studies are conducted, using a continuous domain test bench. First, the effect of sub-sampling is analyzed. Then, a methodology to compute approximate values for the features using a surrogate model is proposed, and validated from the point of view of the classification of the test functions. It is shown that when only small computational budgets are available, using surrogate models as proxies to compute the features can be beneficial.