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Conference Papers Year : 2020

Versatile black-box optimization

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.

Dates and versions

hal-03049284 , version 1 (09-12-2020)

Identifiers

Cite

Jialin Liu, Antoine Moreau, Mike Preuss, Jeremy Rapin, Baptiste Roziere, et al.. Versatile black-box optimization. GECCO '20: Genetic and Evolutionary Computation Conference, Jul 2020, Cancún Mexico, France. pp.620-628, ⟨10.1145/3377930.3389838⟩. ⟨hal-03049284⟩
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