  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.
