, A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012, p.2599, 2010.
, An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion, Workshop on Bayesian Optimization at NIPS 2013, 2013.
, Robots that can adapt like animals, Nature, vol.26, issue.7553, 2015.
DOI : 10.1038/nrn2332
URL : https://hal.archives-ouvertes.fr/hal-01158243
, Unveiling innovative design principles by means of multiple connicting objectives, Engineering Optimization, 2003.
, XFOIL airfoil simulator. raphael.mit, 2013.
, CFD-based optimization for automotive aerodynamics. Optimization and Computational Fluid Dynamics, 2008.
DOI : 10.1007/978-3-540-72153-6_7
, Recent advances in surrogate-based optimization, Progress in Aerospace Sciences, 2009.
DOI : 10.1016/j.paerosci.2008.11.001
URL : http://eprints.soton.ac.uk/65935/1/Forr_09.pdf
, 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
, CMAES Version 3.61.beta. https, 2008.
, Completely Derandomized Self-Adaptation in Evolution Strategies, Evolutionary Computation, vol.9, issue.2, 2001.
DOI : 10.1016/0004-3702(95)00124-7
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.7462
, Automated Antenna Design with Evolutionary Algorithms, Space 2006, 2006.
DOI : 10.2514/6.2006-7242
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.9841
, A comprehensive survey of tness approximation in evolutionary computation, 2005.
, 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
, Illuminating search spaces by mapping elites. arXiv preprint:1504, p.4909, 2015.
, Encouraging Behavioral Diversity in Evolutionary Robotics: An Empirical Study, Evolutionary Computation, vol.341, issue.1, 2012.
DOI : 10.1016/0020-0190(92)90136-J
URL : https://hal.archives-ouvertes.fr/hal-00687609
, 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
URL : http://arxiv.org/abs/1412.1897
, Low-discrepancy and low-dispersion sequences, Journal of Number Theory, vol.30, issue.1, 1988.
DOI : 10.1016/0022-314X(88)90025-X
URL : http://doi.org/10.1016/0022-314x(88)90025-x
, Airfoil Design Under Uncertainty with Robust Geometric Parameterization, 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2009.
DOI : 10.2514/6.2009-2270
, Quality Diversity: A New Frontier for Evolutionary Computation, Frontiers in Robotics and AI, vol.64, 2016.
DOI : 10.1007/s10846-011-9542-z
, Gaussian Process Regression and Classiication Toolbox, 2016.
, Gaussian Process for Machine Learning, Gaussian Process for Machine Learning, 2006.
, Taking the Human Out of the Loop: A Review of Bayesian Optimization, Proc. IEEE, 2016.
DOI : 10.1109/JPROC.2015.2494218
, Parametric Airfoils and Wings, Recent Development of Aerodynamic Design, 1999.
DOI : 10.1007/978-3-322-89952-1_4
, Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design, Proceedings of the 27th International Conference on Machine Learning, 2010.
, A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks, Artificial Life, vol.21, issue.2, 2009.
DOI : 10.1109/5.784219