@inproceedings{13789d0fa9a44ac5a618042391483624,
title = "Permuted orthogonal block-diagonal transformation matrices for large scale optimization benchmarking",
abstract = "We propose a general methodology to construct large-scale testbeds for the benchmarking of continuous optimization algorithms. Our approach applies an orthogonal transformation on raw functions that involve only a linear number of operations in order to obtain large scale optimization benchmark problems. The orthogonal transformation is sampled from a parametrized family of transformations that are the product of a permutation matrix times a block-diagonal matrix times a permutation matrix. We investigate the impact of the different parameters of the transformation on its shape and on the difficulty of the problems for separable CMA-ES. We illustrate the use of the above defined transformation in the BBOB-2009 testbed as replacement for the expensive orthogonal (rotation) matrices. We also show the practicability of the approach by studying the computational cost and its applicability in a large scale setting.",
keywords = "Benchmarking, Continuous optimization, Large scale optimization",
author = "\{El Hara\}, \{Ouassim Ait\} and Anne Auger and Nikolaus Hansen",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 2016 Genetic and Evolutionary Computation Conference, GECCO 2016 ; Conference date: 20-07-2016 Through 24-07-2016",
year = "2016",
month = jul,
day = "20",
doi = "10.1145/2908812.2908937",
language = "English",
series = "GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference",
publisher = "Association for Computing Machinery, Inc",
pages = "189--196",
editor = "Tobias Friedrich",
booktitle = "GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference",
}