  Instance-specific algorithm selection (AS) deals with the automatic selection
of an algorithm from a fixed set of candidates most suitable for a specific
instance of an algorithmic problem class, where "suitability" often refers to
an algorithm's runtime. Over the past years, a plethora of algorithm selectors
have been proposed. As an algorithm selector is again an algorithm solving a
specific problem, the idea of algorithm selection could also be applied to AS
algorithms, leading to a meta-AS approach: Given an instance, the goal is to
select an algorithm selector, which is then used to select the actual algorithm
for solving the problem instance. We elaborate on consequences of applying AS
on a meta-level and identify possible problems. Empirically, we show that
meta-algorithm-selection can indeed prove beneficial in some cases. In general,
however, successful AS approaches have problems with solving the meta-level
problem.
