  In this paper, we build upon previous work on designing informative and
efficient Exploratory Landscape Analysis features for characterizing problems'
landscapes and show their effectiveness in automatically constructing algorithm
selection models in continuous black-box optimization problems. Focussing on
algorithm performance results of the COCO platform of several years, we
construct a representative set of high-performing complementary solvers and
present an algorithm selection model that - compared to the portfolio's single
best solver - on average requires less than half of the resources for solving a
given problem. Therefore, there is a huge gain in efficiency compared to
classical ensemble methods combined with an increased insight into problem
characteristics and algorithm properties by using informative features. Acting
on the assumption that the function set of the Black-Box Optimization Benchmark
is representative enough for practical applications the model allows for
selecting the best suited optimization algorithm within the considered set for
unseen problems prior to the optimization itself based on a small sample of
function evaluations. Note that such a sample can even be reused for the
initial population of an evolutionary (optimization) algorithm so that even the
feature costs become negligible.
