Features for Exploiting Black-Box Optimization Problem Structure.

Black-box optimization (BBO) problems arise in numerous
scientic and engineering applications and are characterized by compu-
tationally intensive objective functions, which severely limit the number
of evaluations that can be performed. We present a robust set of features
that analyze the tness landscape of BBO problems and show how an
algorithm portfolio approach can exploit these general, problem indepen-
dent features and outperform the utilization of any single minimization
search strategy. We test our methodology on data from the GECCO
Workshop on BBO Benchmarking 2012, which contains 21 state-of-the-
art solvers run on 24 well-established functions.