ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1903.06396
24
27

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

15 March 2019
Ouassim Ait ElHara
Konstantinos Varelas
Duc Manh Nguyen
Tea Tušar
D. Brockhoff
N. Hansen
A. Auger
ArXivPDFHTML
Abstract

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.

View on arXiv
Comments on this paper