Versatile Black-Box Optimization

• URL: http://arxiv.org/abs/2004.14014v1
• Date: Wed, 29 Apr 2020 08:20:36 GMT
• Title: Versatile Black-Box Optimization
• Authors: Jialin Liu, Antoine Moreau, Mike Preuss, Baptiste Roziere, Jeremy Rapin, Fabien Teytaud, Olivier Teytaud
• Abstract summary: 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.
• Score: 6.92253842648213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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