CMA-ES for Hyperparameter Optimization of Deep Neural Networks

25 Apr 2016Ilya LoshchilovFrank Hutter

Hyperparameters of deep neural networks are often optimized by grid search, random search or Bayesian optimization. As an alternative, we propose to use the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which is known for its state-of-the-art performance in derivative-free optimization... (read more)

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