  Performance complementarity of solvers available to tackle black-box
optimization problems gives rise to the important task of algorithm selection
(AS). Automated AS approaches can help replace tedious and labor-intensive
manual selection, and have already shown promising performance in various
optimization domains. Automated AS relies on machine learning (ML) techniques
to recommend the best algorithm given the information about the problem
instance. Unfortunately, there are no clear guidelines for choosing the most
appropriate one from a variety of ML techniques. Tree-based models such as
Random Forest or XGBoost have consistently demonstrated outstanding performance
for automated AS. Transformers and other tabular deep learning models have also
been increasingly applied in this context.
  We investigate in this work the impact of the choice of the ML technique on
AS performance. We compare four ML models on the task of predicting the best
solver for the BBOB problems for 7 different runtime budgets in 2 dimensions.
While our results confirm that a per-instance AS has indeed impressive
potential, we also show that the particular choice of the ML technique is of
much minor importance.
