论文标题
多功能黑盒优化
Versatile Black-Box Optimization
论文作者
论文摘要
使用问题描述符自动选择正确的算法是组合优化的经典组成部分。它也是使进化算法快速，强大和多功能的好工具。我们提出了shiwa，这是一种在离散和连续，嘈杂且无噪声，顺序和平行的，黑盒优化的算法。我们的算法与Yabbob上的竞争对手，BBOB可比测试床以及某些变体的竞争对手进行了比较，然后在几个现实世界测试床上进行了验证。
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.
