摘要翻译：
高效自然进化策略(eNES)是传统进化算法的一种新选择，它利用自然梯度来适应变异分布。与以往基于自然梯度的进化算法不同，eNES采用快速算法计算精确Fisher信息矩阵的逆，从而提高了进化梯度估计的鲁棒性和性能，即使在高维环境下也是如此。eNES的其他新方面包括最佳适应度基线和重要性混合（一种用很少的适应度评估更新总体的程序）。该算法在单峰和多峰基准上都产生了竞争性的结果。
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英文标题：
《Efficient Natural Evolution Strategies》
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作者：
Yi Sun and Daan Wierstra and Tom Schaul and Juergen Schmidhuber
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Efficient Natural Evolution Strategies (eNES) is a novel alternative to conventional evolutionary algorithms, using the natural gradient to adapt the mutation distribution. Unlike previous methods based on natural gradients, eNES uses a fast algorithm to calculate the inverse of the exact Fisher information matrix, thus increasing both robustness and performance of its evolution gradient estimation, even in higher dimensions. Additional novel aspects of eNES include optimal fitness baselines and importance mixing (a procedure for updating the population with very few fitness evaluations). The algorithm yields competitive results on both unimodal and multimodal benchmarks.
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PDF链接：
https://arxiv.org/pdf/1209.5853