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Random Search for Hyper-Parameter Optimization

Journal of Machine Learning Research 13 (2012) 281-305 Submitted 3/11; Revised 9/11; Published 2/12. Random Search for Hyper-Parameter Optimization James Bergstra JAMES . BERGSTRA @ UMONTREAL . CA. Yoshua Bengio YOSHUA . BENGIO @ UMONTREAL . CA. De partement d'Informatique et de recherche ope rationnelle Universite de Montre al Montre al, QC, H3C 3J7, Canada Editor: Leon Bottou Abstract Grid Search and manual Search are the most widely used strategies for Hyper-Parameter optimiza- tion. This paper shows empirically and theoretically that randomly chosen trials are more efficient for Hyper-Parameter Optimization than trials on a grid. Empirical evidence comes from a compar- ison with a large previous study that used grid Search and manual Search to configure neural net- works and deep belief networks.

We anticipate that growing interest in large hierarchical models will place an increasing burden on techniques for hyper-parameter optimization; this work shows that random search is a natural base-line against which to judge progress in the development of adaptive (sequential) hyper-parameter optimization algorithms.

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