  Algorithm-selection (AS) methods are essential in order to obtain the best
performance from a portfolio of solvers over large sets of instances. However,
many AS methods rely on an analysis phase, e.g. where features are computed by
sampling solutions and used as input in a machine-learning model. For AS to be
efficient, it is therefore important that this analysis phase is not
computationally expensive. We propose a method for identifying easy instances
which can be solved quickly using a generalist solver without any need for
algorithm-selection. This saves computational budget associated with
feature-computation which can then be used elsewhere in an AS pipeline, e.g.,
enabling additional function evaluations on hard problems. Experiments on the
BBOB dataset in two settings (batch and streaming) show that identifying easy
instances results in substantial savings in function evaluations. Re-allocating
the saved budget to hard problems provides gains in performance compared to
both the virtual best solver (VBS) computed with the original budget, the
single best solver (SBS) and a trained algorithm-selector.
