Versatile black-box optimization

Liu，Jialin1; Moreau，Antoine2; Preuss，Mike3; Rapin，Jeremy4; Roziere，Baptiste4; Teytaud，Fabien5; Teytaud，Olivier6

10.1145/3377930.3389838

2020-06-25

GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference

620-628

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.

Black-box optimization Gradient-free algorithms Open source platform Portfolio algorithm

第一

英语

EI

20204009295522

Evolutionary algorithms ; Testbeds

Computer Applications:723.5 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Optimization Techniques:921.5

2-s2.0-85091748012

Scopus

1.Southern University of Science and Technology,Shenzhen,China
2.Universite Clermont Auvergne,CNRS,SIGMA Clermont,Institut Pascal Clermont-Ferrand,France
3.LIACS,Universiteit Leiden,Netherlands
4.Facebook AI Research,Paris-Dauphine University,Paris,France
5.Univ. Littoral Cote d'Opale,Calais,France
6.Facebook AI Research,Paris,France

Liu，Jialin,Moreau，Antoine,Preuss，Mike,et al. Versatile black-box optimization[C],2020:620-628.