2019

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

Elhara, Ouassim, Varelas, Konstantinos, Nguyen, Duc et al.

Understand

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.

Built on

  • N. Hansen, S. Finck, R. Ros, and A. Auger (2009). Real-parameter black-box optimization benchmarking 2009: Noiseless functions definitions . Research Report RR-6829 , Inria, updated February 2010

    2010

    Earlier work this paper cites.

  • O. Ait Elhara, A. Auger, N. Hansen (2016). Permuted Orthogonal Block-Diagonal Transformation Matrices for Large Scale Optimization Benchmarking . GECCO 2016, Jul 2016, Denver, United States

    2016

    Earlier work this paper cites.

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