深度学习中的神经进化：中立性的作用
Neuroevolution in Deep Learning: The Role of Neutrality
摘要：
各种方法已经应用于人工深度神经网络（DNN）的架构配置和学习或训练。对于大多数问题和应用，这些方法在DNN的成功或失败中起着至关重要的作用。进化算法（EA）作为DNN自动优化的计算可行方法正在获得动力。神经进化是一个术语，它描述了使用EA自动配置和训练DNN的过程。然而，通过进化算法自动设计和/或训练这些现代神经网络在计算上是昂贵的。木村的中性分子进化理论指出，大多数分子水平的进化变化是选择性中性突变随机固定的结果。从一个基因到另一个基因的突变是中性的，如果它不影响表型。本文讨论了在特定条件下，中性如何有助于加速深度神经网络的训练/设计。
英文摘要：
A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNN). These methods play a crucial role in the success or failure of the DNN for most problems and applications. Evolutionary Algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimisation of DNNs. Neuroevolution is a term which describes these processes of automated configuration and training of DNNs using EAs. However, the automatic design and/or training of these modern neural networks through evolutionary algorithms is computanalli expensive. Kimura's neutral theory of molecular evolution states that the majority of evolutionary changes at molecular level are the result of random fixation of selectively neutral mutations. A mutation from one gene to another is neutral if it does not affect the phenotype. This work discusses how neutrality, given certain conditions, can help to speed up the training/design of deep neural networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
