With neural networks having demonstrated their versatility and benefits, the need for their optimal performance is as prevalent as ever. A defining characteristic, hyperparameters, can greatly affect its performance. Thus engineers go through a process, tuning, to identify and implement optimal hyperparameters. That being said, excess amounts of manual effort are required for tuning network architectures, training configurations, and preprocessing settings such as Byte Pair Encoding (BPE). In this study, we propose an automatic tuning method modeled after Darwin's Survival of the Fittest Theory via a Genetic Algorithm (GA). Research results show that the proposed method, a GA, outperforms a random selection of hyperparameters.

中文翻译：

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机器翻译中神经网络超参数优化的遗传算法研究

随着神经网络展示了其多功能性和优势，对其最佳性能的需求与以往一样普遍。一个定义特征，超参数，可以极大地影响其性能。因此，工程师通过一个过程，调整，以识别和实施最佳超参数。话虽如此，调整网络架构、训练配置和预处理设置（如字节对编码 (BPE)）需要大量的手动工作。在这项研究中，我们提出了一种通过遗传算法 (GA) 模仿达尔文适者生存理论的自动调整方法。研究结果表明，所提出的 GA 方法优于随机选择的超参数。