COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite - Inria - Institut national de recherche en sciences et technologies du numérique
Pré-Publication, Document De Travail Année : 2019

COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite

Konstantinos Varelas
Dimo Brockhoff
Nikolaus Hansen
Anne Auger
  • Fonction : Auteur
  • PersonId : 751513
  • IdHAL : anne-auger

Résumé

The bbob-largescale test suite, containing 24 single-objective functions in continuous domain, extends the well-known single-objective noiseless bbob test suite, which has been used since 2009 in the BBOB workshop series, to large dimension. The core idea is to make the rotational transformations R, Q in search space that appear in the bbob test suite computationally cheaper while retaining some desired properties. This documentation presents an approach that replaces a full rotational transformation with a combination of a block-diagonal matrix and two permutation matrices in order to construct test functions whose computational and memory costs scale linearly in the dimension of the problem.
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Dates et versions

hal-02068407 , version 1 (14-03-2019)
hal-02068407 , version 2 (26-03-2019)

Identifiants

Citer

Ouassim Ait Elhara, Konstantinos Varelas, Duc Hung Nguyen, Tea Tušar, Dimo Brockhoff, et al.. COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite. 2019. ⟨hal-02068407v1⟩
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