Oskar Girardin

Centre de Mathématiques Appliquées (CMAP)

Route de Saclay

91128 Palaiseau

Hello! I am Oskar, a PhD student at Inria and Ecole polytechnique in the RandOpt team. My supervisors are Nikolaus Hansen and Dimo Brockhoff.

My research focuses primarily on continuous black-box (derivative-free) optimization algorithms with a focus on Evolutionary Strategies. In particular, I am working on the Covariance Matrix-Adaptation Evolution Strategy (CMA-ES).

My research interests also include (but are not limited to): Constrained and/or Noisy Optimization, Machine Learning, Reinforcement Learning and Benchmarking.

News

Jul 18, 2025

Attended GECCO ‘25 and presented in the track on Evolutionary Numerical Optimization and in the Workshop on Black Box Optimization Benchmarking 2025.

Apr 10, 2025

The paper Classification-Based Linear Surrogate Modeling of Constraints for AL-CMA-ES got accepted in the proceedings of GECCO ‘25! 🎉

Selected publications

Classification-Based Linear Surrogate Modeling of Constraints for AL-CMA-ES

Oskar Girardin, Nikolaus Hansen, Dimo Brockhoff, and Anne Auger

In Proceedings of the Genetic and Evolutionary Computation Conference, Malaga, Spain, 2025

Abs Bib PDF

We introduce linear surrogate functions for modeling inequality constraints to solve constrained blackbox optimization problems with the Augmented Lagrangian CMA-ES. Each surrogate is constructed from a binary classifier that predicts the sign of the constraint value. The classifier, and consequently the resulting algorithm, is invariant under sign preserving transformations of the constraint values and can handle binary, flat, and deceptive constraints. Somewhat surprisingly, we find that adopting a sign-based classification model of the constraints allows to solve classes of constrained problems which can not be solved with the original Augmented Lagrangian method using the true constraint value.
@inproceedings{10.1145/3712256.3726435, author = {Girardin, Oskar and Hansen, Nikolaus and Brockhoff, Dimo and Auger, Anne}, title = {Classification-Based Linear Surrogate Modeling of Constraints for AL-CMA-ES}, year = {2025}, isbn = {9798400714658}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3712256.3726435}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference}, pages = {728–736}, location = {Malaga, Spain}, numpages = {9}, }
Benchmarking CMA-ES under Additive and Subtractive Noise on the BBOB Testbed

Oskar Girardin

In Proceedings of the Genetic and Evolutionary Computation Conference Companion, Malaga, Spain, 2025

Abs Bib PDF

We benchmark a non-elitist CMA-ES algorithm on the BBOB testbed with additive and subtractive noise. In particular, we consider the case where re-evaluated solutions produce the same observed function value. As a comparison, we benchmark a version of CMA-ES with resampling, which aims at reducing the effective noise level. We find CMA-ES to be more sensitive to subtractive noise than to additive noise in dimensions 2, 3, 5, 10, 20 and 40. Resampling for CMA-ES appears to be detrimental for low noise levels, while it is beneficial for high noise levels.
@inproceedings{10.1145/3712255.3734332, author = {Girardin, Oskar}, title = {Benchmarking CMA-ES under Additive and Subtractive Noise on the BBOB Testbed}, year = {2025}, isbn = {9798400714641}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3712255.3734332}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}, pages = {1867–1874}, numpages = {8}, keywords = {benchmarking, black-box optimization, randomized optimization}, location = {Malaga, Spain}, series = {GECCO '25 Companion}, }