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.
Original language | English |
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Title of host publication | GECCO 2020 : Proceedings of the 2020 Genetic and Evolutionary Computation Conference |
Editors | Carlos Artemio COELLO COELLO |
Publisher | Association for Computing Machinery |
Pages | 620-628 |
Number of pages | 9 |
ISBN (Electronic) | 9781450371285 |
DOIs | |
Publication status | Published - 26 Jun 2020 |
Externally published | Yes |
Event | 2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - Cancun, Mexico Duration: 8 Jul 2020 → 12 Jul 2020 |
Conference
Conference | 2020 Genetic and Evolutionary Computation Conference, GECCO 2020 |
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Country/Territory | Mexico |
City | Cancun |
Period | 8/07/20 → 12/07/20 |
Bibliographical note
Publisher Copyright:© 2020 ACM.
Funding
Author J. Liu was supported by the National Key R&D Program of China (Grant No. 2017YFC0804003), the National Natural Science Foundation of China (Grant No. 61906083), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), the Science and Technology Innovation Committee Foundation of Shenzhen (Grant No. JCYJ20190809121403553), the Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531) and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).
Keywords
- Black-box optimization
- Gradient-free algorithms
- Open source platform
- Portfolio algorithm