  One of the most challenging problems in evolutionary computation is to select
from its family of diverse solvers one that performs well on a given problem.
This algorithm selection problem is complicated by the fact that different
phases of the optimization process require different search behavior. While
this can partly be controlled by the algorithm itself, there exist large
differences between algorithm performance. It can therefore be beneficial to
swap the configuration or even the entire algorithm during the run. Long deemed
impractical, recent advances in Machine Learning and in exploratory landscape
analysis give hope that this dynamic algorithm configuration~(dynAC) can
eventually be solved by automatically trained configuration schedules. With
this work we aim at promoting research on dynAC, by introducing a simpler
variant that focuses only on switching between different algorithms, not
configurations. Using the rich data from the Black Box Optimization
Benchmark~(BBOB) platform, we show that even single-switch dynamic Algorithm
selection (dynAS) can potentially result in significant performance gains. We
also discuss key challenges in dynAS, and argue that the BBOB-framework can
become a useful tool in overcoming these.
