K. Varelas, A. Auger, D. Brockhoff, N. Hansen, O. Ait-elhara et al., A Comparative Study of Large-Scale Variants of CMA-ES: PPSN XV, pp.3-15, 2018.

N. Hansen, A. Auger, O. Mersmann, T. Tusar, and D. Brockhoff, COCO: A platform for comparing continuous optimizers in a black-box setting, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01294124

A. Elhara, O. Auger, A. Hansen, and N. , Permuted orthogonal block-diagonal transformation matrices for large scale optimization benchmarking, Genetic and Evolutionary Computation Conference, pp.189-196, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01308566

Y. Akimoto and N. Hansen, Online model selection for restricted covariance matrix adaptation. In: Parallel Problem Solving from Nature (PPSN 2016), pp.3-13, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01333840

Y. Akimoto and N. Hansen, Projection-based restricted covariance matrix adaptation for high dimension, Genetic and Evolutionary Computation Conference, pp.197-204, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01306551

N. Hansen, A. Auger, O. Mersmann, T. Tu?ar, and D. Brockhoff, COCO: A platform for comparing continuous optimizers in a black-box setting, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01294124

N. Hansen, S. Finck, R. Ros, and A. Auger, Real-parameter black-box optimization benchmarking 2009: Noiseless functions definitions, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00362633

N. Hansen and A. Ostermeier, Completely derandomized self-adaptation in evolution strategies, Evol. Comput, vol.9, issue.2, pp.159-195, 2001.

J. N. Knight and M. Lunacek, Reducing the space-time complexity of the CMA-ES, Genetic and Evolutionary Computation Conference, pp.658-665, 2007.

O. Krause, D. R. Arbonès, and C. Igel, CMA-ES with optimal covariance update and storage complexity, NIPS Proceedings, 2016.

Z. Li and Q. Zhang, A simple yet efficient evolution strategy for large scale black-box optimization, IEEE Transactions on Evolutionary Computation, 2017.

D. C. Liu and J. Nocedal, On the limited memory bfgs method for large scale optimization, Math. Program, vol.45, issue.3, pp.503-528, 1989.

I. Loshchilov, LM-CMA: an alternative to L-BFGS for large scale black-box optimization, Evolutionary Computation, vol.25, pp.143-171, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01218646

I. Loshchilov, A computationally efficient limited memory CMA-ES for large scale optimization, Genetic and Evolutionary Computation Conference, pp.397-404, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00981135

I. Loshchilov, T. Glasmachers, and H. Beyer, Limited-memory matrix adaptation for large scale black-box optimization, 2017.

R. Ros and N. Hansen, A simple modification in CMA-ES achieving linear time and space complexity, Nature, pp.296-305, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00270901

Y. Sun, F. J. Gomez, T. Schaul, and J. Schmidhuber, A linear time natural evolution strategy for non-separable functions, 2011.

T. Suttorp, N. Hansen, and C. Igel, Efficient covariance matrix update for variable metric evolution strategies, Machine Learning, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00369468

K. Tang, X. Yáo, P. N. Suganthan, C. Macnish, Y. P. Chen et al., Benchmark functions for the cec2008 special session and competition on large scale global optimization, 2007.