Evolutionary neural AutoML for deep learning
Jason Liang, Elliot Meyerson, Babak Hodjat, Dan Fink, Karl Mutch, Risto P Miikkulainen · Proceedings of the Genetic and Evolutionary Computation Conference · 2019
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains. However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters. Such a configuration is difficult and as a result, DNNs are often not used to their full potential. In addition, DNNs in commercial applications often need to satisfy real-world design constraints such as size or number of parameters. To make configuration easier, automatic machine learning (AutoML) systems for deep learning have been developed, focusing mostly on optimization of hyperparameters.