In this work, we propose a new variant of natural evolution strategies (NES) for high-dimensional black-box optimization problems. The proposed method, CR-FM-NES, extends a recently proposed state-of-the-art NES, Fast Moving Natural Evolution Strategy (FM-NES), in order to be applicable in high-dimensional problems. CR-FM-NES builds on an idea using a restricted representation of a covariance matrix instead of using a full covariance matrix, while inheriting an efficiency of FM-NES. The restricted representation of the covariance matrix enables CR-FM-NES to update parameters of a multivariate normal distribution in linear time and space complexity, which can be applied to high-dimensional problems. Our experimental results reveal that CR-FM-NES does not lose the efficiency of FM-NES, and on the contrary, CR-FM-NES has achieved significant speedup compared to FM-NES on some benchmark problems. Furthermore, our numerical experiments using 200, 600, and 1000-dimensional benchmark problems demonstrate that CR-FM-NES is effective over scalable baseline methods, VD-CMA and Sep-CMA.
翻译:在这项工作中,我们为高维黑盒优化问题提出了一个新的自然演进战略变体。拟议方法CR-FM-NES扩展了最近提出的最先进的NES、快速移动自然演进战略(FM-NES),以便适用于高层面问题。CR-FM-NES基于一种想法,即使用有限的共变矩阵代表,而不是使用完全的共变矩阵,而同时继承调频-NES的效率。调频矩阵的有限代表性使CR-FM-NES能够更新线性时间和空间复杂度的多变量正常分布参数,这些参数可以应用于高层面问题。我们的实验结果表明,CR-FM-NES并没有丧失调频-NES的效率,相反,CR-FM-NES在一些基准问题上比调频-NES快得多。此外,我们使用200、600和1000维维维基准问题进行的数字实验表明,CR-FM-NES对可计量的基准方法、VD-CMA和Sep-CMA有效。