  Exploratory Landscape Analysis is a powerful technique for numerically
characterizing landscapes of single-objective continuous optimization problems.
Landscape insights are crucial both for problem understanding as well as for
assessing benchmark set diversity and composition. Despite the irrefutable
usefulness of these features, they suffer from their own ailments and
downsides. Hence, in this work we provide a collection of different approaches
to characterize optimization landscapes. Similar to conventional landscape
features, we require a small initial sample. However, instead of computing
features based on that sample, we develop alternative representations of the
original sample. These range from point clouds to 2D images and, therefore, are
entirely feature-free. We demonstrate and validate our devised methods on the
BBOB testbed and predict, with the help of Deep Learning, the high-level,
expert-based landscape properties such as the degree of multimodality and the
existence of funnel structures. The quality of our approaches is on par with
methods relying on the traditional landscape features. Thereby, we provide an
exciting new perspective on every research area which utilizes problem
information such as problem understanding and algorithm design as well as
automated algorithm configuration and selection.
