| Augmented Lagrangian constraint handling for CMA-ES—Case of a single linear constraint A Atamna, A Auger, N Hansen International Conference on Parallel Problem Solving from Nature, 181-191, 2016 | 33 | 2016 |
| How to assess step-size adaptation mechanisms in randomised search N Hansen, A Atamna, A Auger Parallel Problem Solving from Nature–PPSN XIII: 13th International …, 2014 | 31 | 2014 |
| Benchmarking ipop-cma-es-tpa and ipop-cma-es-msr on the bbob noiseless testbed A Atamna Proceedings of the Companion Publication of the 2015 Annual Conference on …, 2015 | 27 | 2015 |
| Linearly convergent evolution strategies via augmented Lagrangian constraint handling A Atamna, A Auger, N Hansen Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic …, 2017 | 24 | 2017 |
| COmparing continuous optimizers: numbbo N Hansen, D Brockhoff, O Mersmann, T Tusar, D Tusar, OA ElHara, ... COCO on Github 10, 2019 | 21 | 2019 |
| On invariance and linear convergence of evolution strategies with augmented Lagrangian constraint handling A Atamna, A Auger, N Hansen Theoretical Computer Science 832, 68-97, 2020 | 8 | 2020 |
| Analysis of linear convergence of a (1+ 1)-ES with augmented Lagrangian constraint handling A Atamna, A Auger, N Hansen Proceedings of the Genetic and Evolutionary Computation Conference 2016, 213-220, 2016 | 8 | 2016 |
| SPI-GCN: A simple permutation-invariant graph convolutional network A Atamna, N Sokolovska, JC Crivello | 7 | 2019 |
| COmparing continuous optimizers: numbbo/COCO on Github (2019) N Hansen, D Brockhoff, O Mersmann, T Tusar, D Tusar, OA ElHara, ... Search in, 0 | 7 | |
| A principled approach to analyze expressiveness and accuracy of graph neural networks A Atamna, N Sokolovska, JC Crivello Advances in Intelligent Data Analysis XVIII: 18th International Symposium on …, 2020 | 5 | 2020 |
| COmparing Continuous Optimizers: numbbo/COCO on Github, March 2019 N Hansen, D Brockhoff, O Mersmann, T Tusar, D Tusar, OA ElHara, ... URL https://doi. org/10 5281, 2019 | 5 | 2019 |
| HRI-RNN: a user-robot dynamics-oriented RNN for engagement decrease detection A Atamna, C Clavel INTERSPEECH 2020, 2020 | 3 | 2020 |
| A methodology for building scalable test problems for continuous constrained optimization P Sampaio, N Hansen, D Brockhoff, A Auger, A Atamna Book of Abstracts, 104, 2017 | 3 | 2017 |
| ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation A Pendyala, J Dettmer, T Glasmachers, A Atamna International Conference on Machine Learning, Optimization, and Data Science …, 2023 | 2 | 2023 |
| Convex optimization with an interpolation-based projection and its application to deep learning R Akrour, A Atamna, J Peters Machine Learning 110, 2267-2289, 2021 | 2 | 2021 |
| Analysis of Randomized Adaptive Algorithms for Black-Box Continuous Constrained Optimization A Atamna Université Paris-Saclay (ComUE), 2017 | 2 | 2017 |
| Benchmarking several strategies to update the penalty parameters in AL-CMA-ES on the bbob-constrained testbed P Dufossé, A Atamna Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2022 | 1 | 2022 |
| Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering A Pendyala, A Atamna, T Glasmachers arXiv preprint arXiv:2404.02577, 2024 | | 2024 |
Anne AugerInria, CMAP Ecole Polytechnique, IP ParisAdresse e-mail validée de inria.fr
