Bibliography

Publications of the year

Articles in International Peer-Reviewed Journals

• 1Y. Akimoto, A. Auger, N. Hansen.
  
  Quality Gain Analysis of the Weighted Recombination Evolution Strategy on General Convex Quadratic Functions, in: Theoretical Computer Science, 2018. [ DOI : 10.1016/j.tcs.2018.05.015 ]
  
  https://hal.inria.fr/hal-01662568
• 2A. Atamna, A. Auger, N. Hansen.
  
  On Invariance and Linear Convergence of Evolution Strategies with Augmented Lagrangian Constraint Handling, in: Theoretical Computer Science, November 2018.
  
  https://hal.inria.fr/hal-01660728

Invited Conferences

• 3C. Touré, A. Auger, D. Brockhoff, N. Hansen.
  
  On Bi-Objective convex-quadratic problems, in: 10th International Conference on Evolutionary Multi-Criterion Optimization, East Lansing, Michigan, United States, March 2019, https://arxiv.org/abs/1812.00289.
  
  https://hal.inria.fr/hal-01942159

International Conferences with Proceedings

• 4Y. Akimoto, A. Auger, T. Glasmachers.
  
  Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)-ES with 1/5 Success Rule, in: Proceedings of the GECCO 2018 Conference, Kyoto, Japan, 2018, https://arxiv.org/abs/1802.03209.
  
  https://hal.inria.fr/hal-01778116
• 5A. Blelly, M. Felipe-Gomes, A. Auger, D. Brockhoff.
  
  Stopping Criteria, Initialization, and Implementations of BFGS and their Effect on the BBOB Test Suite, in: GECCO '18 Companion, Kyoto, Japan, July 2018. [ DOI : 10.1145/3205651.3208303 ]
  
  https://hal.inria.fr/hal-01811588
• 6K. Varelas, A. Auger, D. Brockhoff, N. Hansen, O. A. Elhara, Y. Semet, R. Kassab, F. Barbaresco.
  
  A Comparative Study of Large-scale Variants of CMA-ES, in: PPSN XV 2018 - 15th International Conference on Parallel Problem Solving from Nature, Coimbra, Portugal, LNCS, September 2018, vol. 11101, pp. 3-15. [ DOI : 10.1007/978-3-319-99253-2_1 ]
  
  https://hal.inria.fr/hal-01881454

Other Publications

• 7Y. Akimoto, A. Auger, N. Hansen.
  
  An ODE Method to Prove the Geometric Convergence of Adaptive Stochastic Algorithms, November 2018, https://arxiv.org/abs/1811.06703 - working paper or preprint.
  
  https://hal.inria.fr/hal-01926472
• 8Y. Akimoto, N. Hansen.
  
  CMA-ES and Advanced Adaptation Mechanisms, July 2018, GECCO '18 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion.
  
  https://hal.inria.fr/hal-01959479
• 9D. Brockhoff.
  
  GECCO 2018 tutorial on evolutionary multiobjective optimization, July 2018, GECCO '18 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion.
  
  https://hal.inria.fr/hal-01943586
• 10D. Brockhoff, T. Tusar, A. Auger, N. Hansen.
  
  Using Well-Understood Single-Objective Functions in Multiobjective Black-Box Optimization Test Suites, January 2019, https://arxiv.org/abs/1604.00359 - ArXiv e-prints, arXiv:1604.00359.
  
  https://hal.inria.fr/hal-01296987
• 11N. Hansen.
  
  A Practical Guide to Experimentation (and Benchmarking), July 2018, GECCO '18 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion.
  
  https://hal.inria.fr/hal-01959453

References in notes

• 12Y. 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.
• 13Y. 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.
• 14D. 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.
• 15A. Atamna, A. Auger, N. Hansen.
  
  Linearly Convergent Evolution Strategies via Augmented Lagrangian Constraint Handling, in: Foundation of Genetic Algorithms (FOGA), 2017.
• 16A. 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, n^o 3, pp. 1589-1624.
• 17J. Bergstra, R. Bardenet, Y. Bengio, B. Kégl.
  
  Algorithms for Hyper-Parameter Optimization, in: Neural Information Processing Systems (NIPS 2011), 2011.
  
  https://hal.inria.fr/hal-00642998/file/draft1.pdf
• 18J. Bergstra, Y. Bengio.
  
  Random search for hyper-parameter optimization, in: Journal of Machine Learning Research, 2012, vol. 13, pp. 281–305.
• 19V. S. Borkar.
  
  Stochastic approximation: a dynamical systems viewpoint, 2008, Cambridge University Press.
• 20V. 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, n^o 2.
• 21C. 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.
• 22G. 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.
• 23J. E. Dennis, R. B. Schnabel.
  
  Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Prentice-Hall, Englewood Cliffs, NJ, 1983.
• 24N. Hansen, A. Auger.
  
  Principled design of continuous stochastic search: From theory to practice, in: Theory and principled methods for the design of metaheuristics, Springer, 2014, pp. 145–180.
• 25N. Hansen, A. Ostermeier.
  
  Completely Derandomized Self-Adaptation in Evolution Strategies, in: Evolutionary Computation, 2001, vol. 9, n^o 2, pp. 159–195.
• 26J. N. Hooker.
  
  Testing heuristics: We have it all wrong, in: Journal of heuristics, 1995, vol. 1, n^o 1, pp. 33–42.
• 27F. Hutter, H. Hoos, K. Leyton-Brown.
  
  An Evaluation of Sequential Model-based Optimization for Expensive Blackbox Functions, in: GECCO (Companion) 2013, ACM, 2013, pp. 1209–1216.
• 28D. S. Johnson.
  
  A theoretician’s guide to the experimental analysis of algorithms, in: Data structures, near neighbor searches, and methodology: fifth and sixth DIMACS implementation challenges, 2002, vol. 59, pp. 215–250.
• 29D. R. Jones, M. Schonlau, W. J. Welch.
  
  Efficient global optimization of expensive black-box functions, in: Journal of Global optimization, 1998, vol. 13, n^o 4, pp. 455–492.
• 30I. 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, n^o 1, 127 p.
• 31H. J. Kushner, G. Yin.
  
  Stochastic approximation and recursive algorithms and applications, Applications of mathematics, Springer, New York, 2003.
  
  http://opac.inria.fr/record=b1099801
• 32I. Loshchilov, F. Hutter.
  
  CMA-ES for hyperparameter optimization of deep neural networks, in: arXiv preprint arXiv:1604.07269, 2016.
• 33P. 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.
• 34S. Meyn, R. Tweedie.
  
  Markov Chains and Stochastic Stability, Springer-Verlag, New York, 1993.
• 35Y. Ollivier, L. Arnold, A. Auger, N. Hansen.
  
  Information-geometric optimization algorithms: A unifying picture via invariance principles, in: Journal Of Machine Learning Research, 2016, accepted.
• 36T. Salimans, J. Ho, X. Chen, I. Sutskever.
  
  Evolution strategies as a scalable alternative to reinforcement learning, in: arXiv preprint arXiv:1703.03864, 2017.
• 37J. Snoek, H. Larochelle, R. P. Adams.
  
  Practical bayesian optimization of machine learning algorithms, in: Neural Information Processing Systems (NIPS 2012), 2012, pp. 2951–2959.
• 38J. 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, n^o 35, pp. 14271–14276.