E. Bradner, F. Iorio, D. , and M. , Parameters tell the design story: Ideation and abstraction in design optimization, Simulation Series, 2014.

E. Brochu, V. Cora, and N. De-freitas, A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint, 2010.

R. Calandra, A. Seyfarth, J. Peters, and M. P. Deisenroth, An experimental comparison of Bayesian optimization for bipedal locomotion, 2014 IEEE International Conference on Robotics and Automation (ICRA), pp.1951-1958, 2014.
DOI : 10.1109/ICRA.2014.6907117

K. Chatzilygeroudis, V. Vassiliades, and J. Mouret, Reset-free Trial-and-Error Learning for Robot Damage Recovery, Robotics and Autonomous Systems, vol.100, pp.1-19, 2017.
DOI : 10.1016/j.robot.2017.11.010

URL : https://hal.archives-ouvertes.fr/hal-01654641

A. Cully, J. Clune, D. Tarapore, and J. Mouret, Robots that can adapt like animals, Nature, vol.26, issue.7553, 2015.
DOI : 10.1038/nrn2332

URL : https://hal.archives-ouvertes.fr/hal-01158243

K. Deb, Unveiling innovative design principles by means of multiple conflicting objectives . Engineering Optimization, 2003.

, ArXiv Preprint Final version published in Evolutionary Computation, pp.10-1162

A. Gaier, J. Asteroth, and . Mouret,

K. Deb and A. Srinivasan, Innovization, Proceedings of the 8th annual conference on Genetic and evolutionary computation , GECCO '06, pp.1629-1636, 2006.
DOI : 10.1145/1143997.1144266

M. Drela, Xfoil airfoil simulator. raphael.mit, 2013.

L. Dumas, CFD-based optimization for automotive aerodynamics. Optimization and Computational Fluid Dynamics, 2008.

M. Emmerich, A. Giotis, M. Özdemir, T. Bäck, and K. Giannakoglou, Metamodel???Assisted Evolution Strategies, International Conference on parallel problem solving from nature, pp.361-370, 2002.
DOI : 10.1007/3-540-45712-7_35

URL : http://www.liacs.nl/~emmerich/pdf/EGO+02.pdf

A. I. Forrester and A. Keane, Recent advances in surrogate-based optimization, Progress in Aerospace Sciences, 2009.
DOI : 10.1016/j.paerosci.2008.11.001

URL : https://eprints.soton.ac.uk/65935/1/Forr_09.pdf

A. Gaier, A. Asteroth, and J. Mouret, Aerodynamic Design Exploration through Surrogate-Assisted Illumination, 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2017.
DOI : 10.2514/6.2009-2270

URL : https://hal.archives-ouvertes.fr/hal-01518786

A. Gaier, A. Asteroth, and J. Mouret, Data-efficient exploration, optimization, and modeling of diverse designs through surrogate-assisted illumination, Proceedings of the Genetic and Evolutionary Computation Conference on , GECCO '17, 2017.
DOI : 10.1109/CVPR.2015.7298640

URL : https://hal.archives-ouvertes.fr/hal-01518698

K. Giannakoglou, D. Papadimitriou, and I. Kampolis, Aerodynamic shape design using evolutionary algorithms and new gradient-assisted metamodels, Computer Methods in Applied Mechanics and Engineering, vol.195, issue.44-47, 2006.
DOI : 10.1016/j.cma.2005.12.008

A. Hagg, A. Asteroth, and T. Bäck, Prototype discovery using quality-diversity, Parallel Problem Solving From Nature (PPSN), 2018.

N. Hansen, Cmaes version 3, 2008.

N. Hansen and A. Ostermeier, Completely Derandomized Self-Adaptation in Evolution Strategies, Evolutionary Computation, vol.9, issue.2, 2001.
DOI : 10.1016/0004-3702(95)00124-7

URL : http://www.mitpressjournals.org/userimages/ContentEditor/1164817256746/lib_rec_form.pdf

M. Hasenjäger and B. Sendhoff, Three dimensional evolutionary aerodynamic design optimization with CMA-ES, Proceedings of the 2005 conference on Genetic and evolutionary computation , GECCO '05, 2005.
DOI : 10.1145/1068009.1068366

G. S. Hornby, J. D. Lohn, and D. S. Linden, Computer-Automated Evolution of an X-Band Antenna for NASA's Space Technology 5 Mission, Evolutionary Computation, vol.3, issue.2, pp.1-23, 2011.
DOI : 10.1109/22.238519

URL : http://www.mitpressjournals.org/userimages/ContentEditor/1164817256746/lib_rec_form.pdf

, Ihpva official speed records, International Human Powered Vehicle Association, 2017.

Y. Jin, A comprehensive survey of fitness approximation in evolutionary computation, Soft Computing, vol.9, issue.1, 2005.
DOI : 10.1007/s00500-003-0328-5

S. Koos, J. Mouret, and S. Doncieux, The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics, IEEE Transactions on Evolutionary Computation, vol.17, issue.1, 2013.
DOI : 10.1109/TEVC.2012.2185849

J. Lehman and K. Stanley, Evolving a diversity of virtual creatures through novelty search and local competition, Proceedings of the 13th annual conference on Genetic and evolutionary computation, GECCO '11, 2011.
DOI : 10.1145/2001576.2001606

J. Lehman and K. O. Stanley, Abandoning Objectives: Evolution Through the Search for Novelty Alone, Evolutionary Computation, vol.7, issue.3, pp.189-223, 2011.
DOI : 10.1016/0165-6074(93)90215-7

Y. Lian, A. Oyama, and M. Liou, Progress in design optimization using evolutionary algorithms for aerodynamic problems, Progress in Aerospace Sciences, 2010.
DOI : 10.1016/j.paerosci.2009.08.003

, Data-Efficient Design Exploration through Surrogate-Assisted Illumination

S. Menzel, M. Olhofer, and B. Sendhoff, Application of free form deformation techniques in evolutionary design optimisation, Proceedings of 6th World Congress on Structural and Multidisciplinary Optimization, 2005.

J. Mouret and J. Clune, Illuminating search spaces by mapping elites. arXiv preprint, pp.1504-04909, 2015.

A. Nguyen, J. Yosinski, C. , and J. , Deep neural networks are easily fooled: High confidence predictions for unrecognizable images, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298640

H. Niederreiter, Low-discrepancy and low-dispersion sequences, Journal of Number Theory, vol.30, issue.1, 1988.
DOI : 10.1016/0022-314X(88)90025-X

, OpenFOAM Computational Fluid Dynamics toolbox. www. openfoam.org, OpenFOAM Foundation, 2017.

M. Padulo, J. Maginot, M. Guenov, and C. Holden, Airfoil Design Under Uncertainty with Robust Geometric Parameterization, 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2009.
DOI : 10.2514/6.2005-4797

R. Pautrat, K. Chatzilygeroudis, and J. Mouret, Bayesian optimization with automatic prior selection for data-efficient direct policy search, Robotics and Automation (ICRA) IEEE International Conference on, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01768279

R. J. Preen and L. Bull, Design Mining Interacting Wind Turbines, Evolutionary Computation, vol.1, issue.1, pp.89-111, 2016.
DOI : 10.1007/s11081-008-9063-1

J. Pugh, L. Soros, and K. Stanley, Quality Diversity: A New Frontier for Evolutionary Computation, Frontiers in Robotics and AI, 2016.
DOI : 10.1007/s10846-011-9542-z

J. Quiñonero-candela and C. E. Rasmussen, A unifying view of sparse approximate Gaussian process regression, Journal of Machine Learning Research, vol.6, pp.1939-1959, 2005.

J. Quiñonero-candela, C. E. Rasmussen, and A. R. Figueiras-vidal, Sparse spectrum Gaussian process regression, Journal of Machine Learning Research, vol.11, pp.1865-1881, 2010.

C. Rasmussen and H. Nickisch, Gaussian process regression and classification toolbox. www.gaussianprocess.org/gpml, 2016.

C. Rasmussen and C. Williams, Gaussian process for machine learning, 2006.

G. Renner and A. Ekárt, Genetic algorithms in computer aided design, Computer-Aided Design, vol.35, issue.8, pp.709-726, 2003.
DOI : 10.1016/S0010-4485(03)00003-4

J. A. Samareh, A Survey of Shape Parameterization Techniques, pp.333-343, 1999.

T. W. Sederberg and S. R. Parry, Free-form deformation of solid geometric models, ACM SIGGRAPH Computer Graphics, vol.20, issue.4, pp.151-160, 1986.
DOI : 10.1145/15886.15903

B. Shahriari, K. Swersky, Z. Wang, R. Adams, and N. De-freitas, Taking the Human Out of the Loop: A Review of Bayesian Optimization, Proceedings of the IEEE, 2016.
DOI : 10.1109/JPROC.2015.2494218

D. Sieger, S. Menzel, and M. Botsch, A comprehensive comparison of shape deformation methods in evolutionary design optimization, Proceedings of the International Conference on Engineering Optimization. Citeseer, 2012.

E. Snelson and Z. Ghahramani, Local and global sparse Gaussian process approximations, In Artificial Intelligence and Statistics, pp.524-531, 2007.

H. Sobieczky, Parametric Airfoils and Wings, 1999.
DOI : 10.1007/978-3-322-89952-1_4

, ArXiv Preprint Final version published in Evolutionary Computation, pp.10-1162

A. Gaier, J. Asteroth, and . Mouret,

N. Srinivas, A. Krause, S. Kakade, and M. Seeger, Gaussian process optimization in the bandit setting: No regret and experimental design, Proceedings of the 27th International Conference on Machine Learning, 2010.

A. Thompson, An evolved circuit, intrinsic in silicon, entwined with physics, International Conference on Evolvable Systems, pp.390-405, 1996.
DOI : 10.1007/3-540-63173-9_61

URL : http://www.cogs.susx.ac.uk/users/adrianth/ices96/paper.ps.Z

V. Vassiliades, K. Chatzilygeroudis, and J. Mouret, Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm, IEEE Transactions on Evolutionary Computation, 2017.
DOI : 10.1109/TEVC.2017.2735550

URL : https://hal.archives-ouvertes.fr/hal-01630627

, Records -land ? world human powered vehicle association, World Human Powered Vehicle Association, 2017.

Z. Zhou, Y. Ong, and P. Nair, Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.37, issue.1, 2007.
DOI : 10.1109/TSMCC.2005.855506