  In this paper, we compare the three most popular algorithms for
hyperparameter optimization (Grid Search, Random Search, and Genetic Algorithm)
and attempt to use them for neural architecture search (NAS). We use these
algorithms for building a convolutional neural network (search architecture).
Experimental results on CIFAR-10 dataset further demonstrate the performance
difference between compared algorithms. The comparison results are based on the
execution time of the above algorithms and accuracy of the proposed models.
