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The Ethereal
Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS
December 12, 2019 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Petro Liashchynskyi, Pavlo Liashchynskyi
arXiv ID
1912.06059
Category
cs.LG: Machine Learning
Cross-listed
cs.NE,
stat.ML
Citations
722
Venue
arXiv.org
Last Checked
4 months ago
Abstract
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.
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