Block Diagonal Natural Evolution Strategies

The Natural Evolution Strategies (NES) family of search algorithms have been shown to be efficient black-box optimizers, but the most powerful version xNES does not scale to problems with more than a few hundred dimensions. And the scalable variant, SNES, potentially ignores important correlations between parameters. This paper introduces Block Diagonal NES (BD-NES), a variant of NES which uses a block diagonal covariance matrix. The resulting update equations are computationally effective on problems with much higher dimensionality than their full-covariance counterparts, while retaining faster convergence speed than methods that ignore covariance information altogether. The algorithm has been tested on the Octopus-arm benchmark, and the experiments section presents performance statistics showing that BDNES achieves better performance than SNES on networks that are too large to be optimized by xNES.