DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization

  title={DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization},
  author={Noor H. Awad and Neeratyoy Mallik and Frank Hutter},
Modern machine learning algorithms crucially rely on several design decisions to achieve strong performance, making the problem of Hyperparameter Optimization (HPO) more important than ever. Here, we combine the advantages of the popular bandit-based HPO method Hyperband (HB) and the evolutionary search approach of Differential Evolution (DE) to yield a new HPO method which we call DEHB. Comprehensive results on a very broad range of HPO problems, as well as a wide range of tabular benchmarks… 
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