An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters

• URL: http://arxiv.org/abs/2303.12797v2
• Date: Tue, 14 May 2024 07:37:28 GMT
• Title: An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters
• Authors: Julie Keisler, El-Ghazali Talbi, Sandra Claudel, Gilles Cabriel,
• Abstract summary: We propose an algorithmic framework to automatically generate efficient deep neural networks. The framework is based on evolving directed acyclic graphs (DAGs) It allows mixtures of different classical operations: convolutions, recurrences and dense layers, but also more newfangled operations such as self-attention.
• Score: 0.23301643766310373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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