Y. Akimoto, A. Auger, and N. Hansen, Comparison-based natural gradient optimization in high dimension, Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp.373-380, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00997835

Y. Akimoto and N. Hansen, Online model selection for restricted covariance matrix adaptation, International Conference on Parallel Problem Solving from Nature, 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

K. Ouassim-ait-elhara, D. H. Varelas, T. Nguyen, D. Tusar, N. Brockhoff et al., COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite, 2019.

N. Hansen, D. Auger, D. Brockhoff, T. Tu?ar, and . Tu?ar, COCO: Performance Assessment, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01315318

N. Hansen, A. Auger, S. Finck, and R. Ros, Real-Parameter Black-Box Optimization Benchmarking 2012: Experimental Setup, 2012.

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, Evolutionary computation, vol.9, pp.159-195, 2001.

N. Hansen, T. Tu?ar, O. Mersmann, A. Auger, and D. Brockhoff, COCO: The Experimental Procedure, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01294167

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. , On the Limited Memory BFGS Method for Large Scale Optimization, Math. Program, vol.45, pp.503-528, 1989.

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, 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

K. Price, Differential evolution vs. the functions of the second ICEO, Proceedings of the IEEE International Congress on Evolutionary Computation, pp.153-157, 1997.

R. Ros and N. Hansen, A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity. In Parallel Problem Solving from, Nature, pp.296-305, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00287367

T. Suttorp, N. Hansen, and C. Igel, Efficient covariance matrix update for variable metric evolution strategies, Machine Learning, vol.75, pp.167-197, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00369468