# Maximum Likelihood-based Online Adaptation of Hyper-parameters in CMA-ES

arXiv: https://arxiv.org/abs/1406.2623 · HTML: https://mlsanity.com/paper/1406.2623/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2014.

## Built on by (most cited first, 1 of 1)

- [Towards a Principled Learning Rate Adaptation for Natural Evolution Strategies](https://mlsanity.com/paper/2112.10680/) (2021)

## Abstract (arXiv)

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is widely accepted as a robust derivative-free continuous optimization algorithm for non-linear and non-convex optimization problems. CMA-ES is well known to be almost parameterless, meaning that only one hyper-parameter, the population size, is proposed to be tuned by the user. In this paper, we propose a principled approach called self-CMA-ES to achieve the online adaptation of CMA-ES hyper-parameters in order to improve its overall performance. Experimental results show that for larger-than-default population size, the default settings of hyper-parameters of CMA-ES are far from being optimal, and that self-CMA-ES allows for dynamically approaching optimal settings.