Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Publications of the year

Doctoral Dissertations and Habilitation Theses

O. Ait Elhara.
Stochastic Black-Box Optimization and Benchmarking in Large Dimensions, Université Paris-Saclay, July 2017.

Articles in International Peer-Reviewed Journals

A. Chotard, A. Auger.
Verifiable Conditions for the Irreducibility and Aperiodicity of Markov Chains by Analyzing Underlying Deterministic Models, in: Bernoulli, 2017,, forthcoming.
Y. Ollivier, L. Arnold, A. Auger, N. Hansen.
Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles, in: Journal of Machine Learning Research, 2017, vol. 18, no 18, pp. 1-65.

International Conferences with Proceedings

Y. Akimoto, A. Auger, N. Hansen.
Quality Gain Analysis of the Weighted Recombination Evolution Strategy on General Convex Quadratic Functions, in: Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, FOGA, Copenhagen, Denmark, January 2017, pp. 111-126. [ DOI : 10.1145/3040718.3040720 ]
A. Atamna, A. Auger, N. Hansen.
Linearly Convergent Evolution Strategies via Augmented Lagrangian Constraint Handling, in: The 14th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XIV), Copenhagen, Denmark, January 2017, pp. 149 - 161. [ DOI : 10.1145/3040718.3040732 ]
D. Brockhoff, A. Auger, N. Hansen, T. Tušar.
Quantitative Performance Assessment of Multiobjective Optimizers: The Average Runtime Attainment Function, in: Evolutionary Multi-Criterion Optimization (EMO 2017), Münster, Germany, LNCS, March 2017, vol. 10173, pp. 103-119. [ DOI : 10.1007/978-3-319-54157-0_8 ]
D. M. Nguyen, N. Hansen.
Benchmarking CMAES-APOP on the BBOB noiseless testbed, in: Proceedings of the 2017 Genetic and Evolutionary Computation Conference Companion (GECCO '17 Companion), Berlin, Germany, July 2017, pp. 1756 - 1763. [ DOI : 10.1145/2908812.2908864 ]
T. Tušar, N. Hansen, D. Brockhoff.
Anytime Benchmarking of Budget-Dependent Algorithms with the COCO Platform, in: IS 2017 - International multiconference Information Society, Ljubljana, Slovenia, October 2017, pp. 1-4.

Other Publications

Y. Akimoto, A. Auger, N. Hansen.
Quality Gain Analysis of the Weighted Recombination Evolution Strategy on General Convex Quadratic Functions, December 2017, Submitted to Journal of Theoretical Computer Science.
A. Atamna, A. Auger, N. Hansen.
On Invariance and Linear Convergence of Evolution Strategies with Augmented Lagrangian Constraint Handling, December 2017, working paper or preprint.
References in notes
Y. Akimoto, N. Hansen.
Online model selection for restricted covariance matrix adaptation, in: International Conference on Parallel Problem Solving from Nature, Springer, 2016, pp. 3–13.
Y. Akimoto, N. Hansen.
Projection-based restricted covariance matrix adaptation for high dimension, in: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, ACM, 2016, pp. 197–204.
D. V. Arnold, J. Porter.
Towards au Augmented Lagrangian Constraint Handling Approach for the (1+1)-ES, in: Genetic and Evolutionary Computation Conference, ACM Press, 2015, pp. 249-256.
A. Atamna, A. Auger, N. Hansen.
Linearly Convergent Evolution Strategies via Augmented Lagrangian Constraint Handling, in: Foundation of Genetic Algorithms (FOGA), 2017.
A. Auger, N. Hansen.
Linear Convergence of Comparison-based Step-size Adaptive Randomized Search via Stability of Markov Chains, in: SIAM Journal on Optimization, 2016, vol. 26, no 3, pp. 1589-1624.
V. S. Borkar.
Stochastic approximation: a dynamical systems viewpoint, 2008, Cambridge University Press.
V. Borkar, S. Meyn.
The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning, in: SIAM Journal on Control and Optimization, January 2000, vol. 38, no 2.
C. A. Coello Coello.
Constraint-handling techniques used with evolutionary algorithms, in: Proceedings of the 2008 Genetic and Evolutionary Computation Conference, ACM, 2008, pp. 2445–2466.
G. Collange, S. Reynaud, N. Hansen.
Covariance Matrix Adaptation Evolution Strategy for Multidisciplinary Optimization of Expendable Launcher Families, in: 13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference, Proceedings, 2010.
J. E. Dennis, R. B. Schnabel.
Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Prentice-Hall, Englewood Cliffs, NJ, 1983.
N. Hansen, A. Ostermeier.
Completely Derandomized Self-Adaptation in Evolution Strategies, in: Evolutionary Computation, 2001, vol. 9, no 2, pp. 159–195.
J. N. Hooker.
Testing heuristics: We have it all wrong, in: Journal of heuristics, 1995, vol. 1, no 1, pp. 33–42.
I. Kriest, V. Sauerland, S. Khatiwala, A. Srivastav, A. Oschlies.
Calibrating a global three-dimensional biogeochemical ocean model (MOPS-1.0), in: Geoscientific Model Development, 2017, vol. 10, no 1, 127 p.
H. J. Kushner, G. Yin.
Stochastic approximation and recursive algorithms and applications, Applications of mathematics, Springer, New York, 2003.
P. MacAlpine, S. Barrett, D. Urieli, V. Vu, P. Stone.
Design and Optimization of an Omnidirectional Humanoid Walk: A Winning Approach at the RoboCup 2011 3D Simulation Competition, in: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI), July 2012.
S. Meyn, R. Tweedie.
Markov Chains and Stochastic Stability, Springer-Verlag, New York, 1993.
T. Salimans, J. Ho, X. Chen, I. Sutskever.
Evolution strategies as a scalable alternative to reinforcement learning, in: arXiv preprint arXiv:1703.03864, 2017.
J. Uhlendorf, A. Miermont, T. Delaveau, G. Charvin, F. Fages, S. Bottani, G. Batt, P. Hersen.
Long-term model predictive control of gene expression at the population and single-cell levels, in: Proceedings of the National Academy of Sciences, 2012, vol. 109, no 35, pp. 14271–14276.