A Simple Yet Efficient Rank One Update for Covariance Matrix Adaptation
This work addresses a computational bottleneck in optimization algorithms for researchers and practitioners, but it is incremental as it builds on existing CMA-ES methods.
The paper tackles the computational inefficiency of covariance matrix adaptation evolution strategies by proposing an efficient approximated rank one update that avoids matrix decomposition, and it generally outperforms or performs competitively with Cholesky CMA-ES in experiments.
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.