In this paper, we propose an efficient approximated rank one update for covariance matrix adaptation evolution strategy (CMA-ES). It makes use of two evolution paths as simple as that of CMA-ES, while avoiding the computational matrix decomposition. We analyze the algorithms' properties and behaviors. We experimentally study the proposed algorithm's performances. It generally outperforms or performs competitively to the Cholesky CMA-ES.
@article{arxiv.1710.03996,
title = {A Simple Yet Efficient Rank One Update for Covariance Matrix Adaptation},
author = {Zhenhua Li and Qingfu Zhang},
journal= {arXiv preprint arXiv:1710.03996},
year = {2017}
}