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

Publications of the year

Doctoral Dissertations and Habilitation Theses

[1]
O. Ait Elhara.
Stochastic Black-Box Optimization and Benchmarking in Large Dimensions, Université Paris-Saclay, July 2017.
https://tel.archives-ouvertes.fr/tel-01615829

Articles in International Peer-Reviewed Journals

[2]
A. Chotard, A. Auger.
Verifiable Conditions for the Irreducibility and Aperiodicity of Markov Chains by Analyzing Underlying Deterministic Models, in: Bernoulli, 2017, https://arxiv.org/abs/1508.01644, forthcoming.
https://hal.inria.fr/hal-01222222
[3]
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.
https://hal.inria.fr/hal-01515898

International Conferences with Proceedings

[4]
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 ]
https://hal.inria.fr/hal-01516326
[5]
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 ]
https://hal.inria.fr/hal-01455379
[6]
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 ]
https://hal.inria.fr/hal-01591151
[7]
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 ]
https://hal.inria.fr/hal-01591423
[8]
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.
https://hal.inria.fr/hal-01629087

Other Publications

[9]
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.
https://hal.inria.fr/hal-01662568
[10]
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.
https://hal.inria.fr/hal-01660728
References in notes
[11]
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.
[12]
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.
[13]
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.
[14]
A. Atamna, A. Auger, N. Hansen.
Linearly Convergent Evolution Strategies via Augmented Lagrangian Constraint Handling, in: Foundation of Genetic Algorithms (FOGA), 2017.
[15]
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.
[16]
V. S. Borkar.
Stochastic approximation: a dynamical systems viewpoint, 2008, Cambridge University Press.
[17]
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.
[18]
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.
[19]
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.
[20]
J. E. Dennis, R. B. Schnabel.
Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Prentice-Hall, Englewood Cliffs, NJ, 1983.
[21]
N. Hansen, A. Ostermeier.
Completely Derandomized Self-Adaptation in Evolution Strategies, in: Evolutionary Computation, 2001, vol. 9, no 2, pp. 159–195.
[22]
J. N. Hooker.
Testing heuristics: We have it all wrong, in: Journal of heuristics, 1995, vol. 1, no 1, pp. 33–42.
[23]
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.
[24]
H. J. Kushner, G. Yin.
Stochastic approximation and recursive algorithms and applications, Applications of mathematics, Springer, New York, 2003.
http://opac.inria.fr/record=b1099801
[25]
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.
[26]
S. Meyn, R. Tweedie.
Markov Chains and Stochastic Stability, Springer-Verlag, New York, 1993.
[27]
T. Salimans, J. Ho, X. Chen, I. Sutskever.
Evolution strategies as a scalable alternative to reinforcement learning, in: arXiv preprint arXiv:1703.03864, 2017.
[28]
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.