@inproceedings {pub5897,
	title = {An Adaptive Re-evaluation Method for Evolution Strategy under Additive Noise},
	author = {Catalin-Viorel Dinu AND Yash J. Patel AND Xavier Bonet-Monroig AND Hao Wang},
	year = {2025},
	month = {July},
	abstract = {The Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is one of the most advanced algorithms in numerical black-box optimization. For noisy objective functions, several approaches were proposed to mitigate the noise, e.g., re-evaluations of the same solution or adapting the population size.
In this paper, we devise a novel method to adaptively choose the optimal re-evaluation number for function values corrupted by additive Gaussian white noise. We derive a theoretical lower bound of the expected improvement achieved in one iteration of CMA-ES, given an estimation of the noise level and the Lipschitz constant of the function{\textquoteright}s gradient.
Solving for the maximum of the lower bound, we obtain a simple expression of the optimal re-evaluation number.
We experimentally compare our method to the state-of-the-art noise-handling methods for CMA-ES on a set of artificial test functions across various noise levels, optimization budgets, and dimensionality. Our method demonstrates significant advantages in terms of the probability of hitting near-optimal function values.},
	publisher = {ACM},
	booktitle = {GECCO {\textquoteright}25: Proceedings of the Genetic and Evolutionary Computation Conference Pages 710 - 718 https://doi.org/10.1145/3712256.3726352}
}