  Choosing automatically the right algorithm using problem descriptors is a
classical component of combinatorial optimization. It is also a good tool for
making evolutionary algorithms fast, robust and versatile. We present Shiwa, an
algorithm good at both discrete and continuous, noisy and noise-free,
sequential and parallel, black-box optimization. Our algorithm is
experimentally compared to competitors on YABBOB, a BBOB comparable testbed,
and on some variants of it, and then validated on several real world testbeds.
