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
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},}
