论文标题
一项用于机器翻译中神经网络高参数优化的遗传算法的研究
A Study of Genetic Algorithms for Hyperparameter Optimization of Neural Networks in Machine Translation
论文作者
论文摘要
由于神经网络证明了它们的多功能性和好处，因此对其最佳性能的需求一如既往地普遍存在。定义的特征，超参数会极大地影响其性能。因此，工程师经过一个过程，调整，识别和实施最佳的超参数。话虽如此，调整网络架构，培训配置和预处理设置（例如字节对编码（BPE））需要过多的手动努力。在这项研究中，我们提出了一种以遗传算法（GA）为基础的Darwin生存的自动调整方法。研究结果表明，所提出的方法A GA的表现优于随机选择超参数。
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.
