Generating more evenly distributed samples in high dimensional search spaces is the major purpose of the recently proposed mirrored sampling technique for evolution strategies. The diversity of the mutation samples is enlarged and the convergence rate is therefore improved by the mirrored sampling. Motivated by the mirrored sampling technique, this article introduces a new derandomized sampling technique called mirrored orthogonal sampling. The performance of this new technique is both theoretically analyzed and empirically studied on the sphere function. In particular, the mirrored orthogonal sampling technique is applied to the well-known Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The resulting algorithm is experimentally tested on the well-known Black-Box Optimization Benchmark (BBOB). By comparing the results from the benchmark, mirrored orthogonal sampling is found to outperform both the standard CMA-ES and its variant using mirrored sampling.

中文翻译：

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协方差矩阵自适应进化策略的镜像正交采样

在高维搜索空间中生成分布更均匀的样本是最近提出的用于进化策略的镜像采样技术的主要目的。通过镜像采样扩大了变异样本的多样性，从而提高了收敛速度。受镜像采样技术的启发，本文介绍了一种新的非随机化采样技术，称为镜像正交采样。这种新技术的性能在球函数上进行了理论分析和实证研究。特别是，镜像正交采样技术被应用于众所周知的协方差矩阵自适应进化策略（CMA-ES）。所得算法在著名的黑盒优化基准 (BBOB) 上进行了实验测试。通过比较基准的结果，