Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context
This work addresses the problem of improving surrogate modeling efficiency for black-box optimization, but it appears incremental as it focuses on landscape analysis without introducing new methods.
The paper investigates how the predictive accuracy of surrogate models relates to features of black-box function landscapes, analyzing data from surrogate-assisted CMA-ES runs on benchmark functions.
Surrogate modeling has become a valuable technique for black-box optimization tasks with expensive evaluation of the objective function. In this paper, we investigate the relationship between the predictive accuracy of surrogate models and features of the black-box function landscape. We also study properties of features for landscape analysis in the context of different transformations and ways of selecting the input data. We perform the landscape analysis of a large set of data generated using runs of a surrogate-assisted version of the Covariance Matrix Adaptation Evolution Strategy on the noiseless part of the Comparing Continuous Optimisers benchmark function testbed.