  In recent years, people from all over the world are suffering from one of the
most severe diseases in history, known as Coronavirus disease 2019, COVID-19
for short. When the virus reaches the lungs, it has a higher probability to
cause lung pneumonia and sepsis. X-ray image is a powerful tool in identifying
the typical features of the infection for COVID-19 patients. The radiologists
and pathologists observe that ground-glass opacity appears in the chest X-ray
for infected patient \cite{cozzi2021ground}, and it could be used as one of the
criteria during the diagnosis process. In the past few years, deep learning has
proven to be one of the most powerful methods in the field of image
classification. Due to significant differences in Chest X-Ray between normal
and infected people \cite{rousan2020chest}, deep models could be used to
identify the presence of the disease given a patient's Chest X-Ray. Many deep
models are complex, and it evolves with lots of input parameters. Designers
sometimes struggle with the tuning process for deep models, especially when
they build up the model from scratch. Genetic Algorithm, inspired by the
biological evolution process, plays a key role in solving such complex
problems. In this paper, I proposed a genetic-based approach to optimize the
Convolutional Neural Network(CNN) for the Chest X-Ray classification task.
