Versatile black-box optimization

Genetic and Evolutionary Computation Conference (GECCO), pp. 620–628

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.

Authors 7

1. Jialin Liu

   Southern University of Science and Technology

   Affiliation as printed

   Southern University of Science and Technology, Shenzhen, China

2. Antoine Moreau

   Université Clermont Auvergne

   Affiliation as printed

   Université Clermont Auvergne, France

3. Mike Preuß Aachen Leiden Institute of Advanced Computer Science [Leiden]

   Leiden University

   Affiliation as printed

   LIACS, Universiteit, Leiden, The Netherlands

   Leiden Institute of Advanced Computer Science [Leiden]

4. Jérémy Rapin

   Université Paris Dauphine-PSL

   Affiliation as printed

   Paris-Dauphine University, Paris, France

5. Baptiste Rozière

   Université Paris Dauphine-PSL

   Affiliation as printed

   Paris-Dauphine University, Paris, France

6. Fabien Teytaud

   Université du littoral côte d'opale

   Affiliation as printed

   Univ. Littoral Cote d'Opale, Calais, France

7. Olivier Teytaud

   Meta (United States)

   Affiliation as printed

   Facebook AI Research, Paris, France