An Adaptive Re-evaluation Method for Evolution Strategy under Additive Noise
<|reference_start|>An Adaptive Re-evaluation Method for Evolution Strategy under Additive Noise: 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'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.<|reference_end|>
@article{dinu2024an,
    title={An Adaptive Re-evaluation Method for Evolution Strategy under Additive
  Noise},
    author={Catalin-Viorel Dinu, Yash J. Patel, Xavier Bonet-Monroig, Hao Wang},
    journal={arXiv preprint arXiv:2409.16757},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16757},
    primaryClass={cs.NE}
}