An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters
This work addresses the challenge of automating neural architecture and hyperparameter optimization for researchers and practitioners, but it appears incremental as it builds on existing evolutionary methods with a more flexible search space.
The paper tackles the problem of automatically generating efficient deep neural networks and optimizing their hyperparameters by proposing an algorithmic framework based on evolving directed acyclic graphs, which allows mixtures of operations like convolutions and self-attention. The results show that the framework found models outperforming established baselines on a time series prediction benchmark.
In this paper, we propose an algorithmic framework to automatically generate efficient deep neural networks and optimize their associated hyperparameters. The framework is based on evolving directed acyclic graphs (DAGs), defining a more flexible search space than the existing ones in the literature. It allows mixtures of different classical operations: convolutions, recurrences and dense layers, but also more newfangled operations such as self-attention. Based on this search space we propose neighbourhood and evolution search operators to optimize both the architecture and hyper-parameters of our networks. These search operators can be used with any metaheuristic capable of handling mixed search spaces. We tested our algorithmic framework with an evolutionary algorithm on a time series prediction benchmark. The results demonstrate that our framework was able to find models outperforming the established baseline on numerous datasets.