Black-box optimization of a previously unknown problem can often prove to be a demanding task. In order for the optimization process to be as efficient as possible, one must first recognize the nature of the problem at hand and then proceed to choose the algorithm exhibiting the best performance for that type of problem. The problem characterization is done via underlying fitness landscape features, which allow to identify similarities and differences between various problems. In this paper we present first steps towards an adaptive landscape analysis. Our approach is aimed at taking a closer look into how features evolve during the optimization process and whether this information can be used to discriminate between different problems. The motivation of our work is to understand if and how one could exploit the information provided by the features to improve on dynamic algorithm selection and configuration. Put differently, our goal is to leverage landscape analysis to adjust the choice of the algorithm on the fly, i.e., during the optimization process itself.
