Bibliography

Publications of the year

Articles in International Peer-Reviewed Journals

• 1Y. Akimoto, N. Hansen.
  Diagonal Acceleration for Covariance Matrix Adaptation Evolution Strategies, in: Evolutionary Computation, 2019, https://arxiv.org/abs/1905.05885, forthcoming. [ DOI : 10.1162/evco_a_00260 ]
  https://hal.inria.fr/hal-01995373

International Conferences with Proceedings

• 2D. Brockhoff, N. Hansen.
  The Impact of Sample Volume in Random Search on the bbob Test Suite, in: GECCO 2019 - The Genetic and Evolutionary Computation Conference, Prague, Czech Republic, July 2019. [ DOI : 10.1145/3319619.3326894 ]
  https://hal.inria.fr/hal-02171213
• 3D. Brockhoff, T. Tušar.
  Benchmarking Algorithms from the platypus Framework on the Biobjective bbob-biobj Testbed, in: GECCO 2019 - The Genetic and Evolutionary Computation Conference, Prague, Czech Republic, July 2019, vol. 7. [ DOI : 10.1145/3319619.3326896 ]
  https://hal.inria.fr/hal-02171136
• 4P. Dufossé, C. Touré.
  Benchmarking MO-CMA-ES and COMO-CMA-ES on the Bi-objective bbob-biobj Testbed, in: GECCO 2019 - The Genetic and Evolutionary Computation Conference, Prague, Czech Republic, July 2019. [ DOI : 10.1145/3319619.3326892 ]
  https://hal.inria.fr/hal-02161252
• 7C. Touré, N. Hansen, A. Auger, D. Brockhoff.
  Uncrowded Hypervolume Improvement: COMO-CMA-ES and the Sofomore framework, in: GECCO 2019 - The Genetic and Evolutionary Computation Conference, Prague, Czech Republic, July 2019, Part of this research has been conducted in the context of a research collaboration between Storengy and Inria. [ DOI : 10.1145/3321707.3321852 ]
  https://hal.inria.fr/hal-02103694
• 8T. Tušar, D. Brockhoff, N. Hansen.
  Mixed-Integer Benchmark Problems for Single-and Bi-Objective Optimization, in: GECCO 2019 -The Genetic and Evolutionary Computation Conference, Prague, Czech Republic, July 2019, submitted to GECCO 2019.
  https://hal.inria.fr/hal-02067932
• 9K. Varelas, M.-A. Dahito.
  Benchmarking Multivariate Solvers of SciPy on the Noiseless Testbed, in: GECCO 2019 - The Genetic and Evolutionary Computation Conference, Prague, Czech Republic, July 2019. [ DOI : 10.1145/3319619.3326891 ]
  https://hal.inria.fr/hal-02160099
• 10K. Varelas.
  Benchmarking Large Scale Variants of CMA-ES and L-BFGS-B on the bbob-largescale Testbed, in: GECCO 2019 - The Genetic and Evolutionary Computation Conference, Prague, Czech Republic, July 2019. [ DOI : 10.1145/3319619.3326893 ]
  https://hal.inria.fr/hal-02160106

Other Publications

• 11D. Brockhoff, T. Tušar, 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
• 12O. A. Elhara, K. Varelas, D. H. Nguyen, T. Tušar, D. Brockhoff, N. Hansen, A. Auger.
  COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite, March 2019, https://arxiv.org/abs/1903.06396 - working paper or preprint.
  https://hal.inria.fr/hal-02068407
• 13S. Mahévas, V. Picheny, P. Lambert, N. Dumoulin, L. Rouan, C. Soulié, D. Brockhoff, S. Lehuta, R. Le Riche, R. Faivre, H. Drouineau.
  A practical guide for conducting calibration and decision-making optimisation with complex ecological models, December 2019, pdf available at https://www.preprints.org/manuscript/201912.0249/v1. [ DOI : 10.20944/preprints201912.0249.v1 ]
  https://hal.inria.fr/hal-02418667

References in notes

• 14Y. 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.
• 15Y. 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.
• 16D. 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.
• 17A. Atamna, A. Auger, N. Hansen.
  Linearly Convergent Evolution Strategies via Augmented Lagrangian Constraint Handling, in: Foundation of Genetic Algorithms (FOGA), 2017.
• 18A. 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.
• 19J. 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
• 20V. S. Borkar.
  Stochastic approximation: a dynamical systems viewpoint, 2008, Cambridge University Press.
• 21V. 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.
• 22C. 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.
• 23G. 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.
• 24J. E. Dennis, R. B. Schnabel.
  Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Prentice-Hall, Englewood Cliffs, NJ, 1983.
• 25N. 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.
• 26N. Hansen, A. Ostermeier.
  Completely Derandomized Self-Adaptation in Evolution Strategies, in: Evolutionary Computation, 2001, vol. 9, n^o 2, pp. 159–195.
• 27J. N. Hooker.
  Testing heuristics: We have it all wrong, in: Journal of heuristics, 1995, vol. 1, n^o 1, pp. 33–42.
• 28F. 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.
• 29D. 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.
• 30D. 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.
• 31I. 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.
• 32H. J. Kushner, G. Yin.
  Stochastic approximation and recursive algorithms and applications, Applications of mathematics, Springer, New York, 2003.
  http://opac.inria.fr/record=b1099801
• 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.