Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context

When solving a real-world optimization problem we often have no information about the analytic form of the objective function. Evaluation of such black-box functions is frequently expensive in terms of time and money (Baerns and Holeňa, 2009; Lee et al., 2016; Zaefferer et al., 2016), which has been for two decades the driving force of research into surrogate modeling of black-box objective functions (Büche et al., 2005; Forrester and Keane, 2009; Jin, 2011). Given a set of observations, a surrogate model can be fitted to approximate the landscape of the black-box function. The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) by Hansen (2006), which we consider the state-of-the-art evolutionary black-box optimizer, has been frequently combined with surrogate models.