  Dynamic algorithm selection aims to exploit the complementarity of multiple
optimization algorithms by switching between them during the search. While
these kinds of dynamic algorithms have been shown to have potential to
outperform their component algorithms, it is still unclear how this potential
can best be realized. One promising approach is to make use of landscape
features to enable a per-run trajectory-based switch. Here, the samples seen by
the first algorithm are used to create a set of features which describe the
landscape from the perspective of the algorithm. These features are then used
to predict what algorithm to switch to.
  In this work, we extend this per-run trajectory-based approach to consider a
wide variety of potential points at which to perform the switch. We show that
using a sliding window to capture the local landscape features contains
information which can be used to predict whether a switch at that point would
be beneficial to future performance. By analyzing the resulting models, we
identify what features are most important to these predictions. Finally, by
evaluating the importance of features and comparing these values between
multiple algorithms, we show clear differences in the way the second algorithm
interacts with the local landscape features found before the switch.
