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.
Cite
@article{arxiv.1903.06396,
title = {COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite},
author = {Ouassim Elhara and Konstantinos Varelas and Duc Nguyen and Tea Tusar and Dimo Brockhoff and Nikolaus Hansen and Anne Auger},
journal= {arXiv preprint arXiv:1903.06396},
year = {2019}
}