  A key challenge in the application of evolutionary algorithms in practice is
the selection of an algorithm instance that best suits the problem at hand.
What complicates this decision further is that different algorithms may be best
suited for different stages of the optimization process. Dynamic algorithm
selection and configuration are therefore well-researched topics in
evolutionary computation. However, while hyper-heuristics and parameter control
studies typically assume a setting in which the algorithm needs to be chosen
while running the algorithms, without prior information, AutoML approaches such
as hyper-parameter tuning and automated algorithm configuration assume the
possibility of evaluating different configurations before making a final
recommendation. In practice, however, we are often in a middle-ground between
these two settings, where we need to decide on the algorithm instance before
the run ("oneshot" setting), but where we have (possibly lots of) data
available on which we can base an informed decision.
  We analyze in this work how such prior performance data can be used to infer
informed dynamic algorithm selection schemes for the solution of pseudo-Boolean
optimization problems. Our specific use-case considers a family of genetic
algorithms.
