Versatile Black-Box Optimization
This work addresses the need for robust and fast evolutionary algorithms in combinatorial optimization, though it appears incremental as it builds on existing testbeds and methods.
The authors tackled the problem of developing a versatile black-box optimization algorithm that works across discrete and continuous, noisy and noise-free, and sequential and parallel settings, resulting in Shiwa, which was experimentally validated on benchmarks like YABBOB and real-world testbeds.
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.