The authors' methodology could be extended to automatically discover other hyperparameters or architectural components of deep learning models in an end-to-end fashion by:
Automated Architecture Search: Integrate the joint evolution approach with neural architecture search (NAS) techniques to automatically discover optimal network architectures, hyperparameters, optimizers, and other components simultaneously.
Dynamic Hyperparameter Tuning: Develop a dynamic hyperparameter tuning mechanism that adapts the discovered optimizers, decay functions, and learning rate schedules based on the model's performance during training, leading to adaptive and efficient optimization.
Multi-Objective Optimization: Extend the methodology to perform multi-objective optimization, considering multiple performance metrics simultaneously to find a balance between different aspects of model performance.
Meta-Learning Techniques: Incorporate meta-learning techniques to enable the discovered components to adapt to new tasks and datasets quickly, enhancing the model's transfer learning capabilities.
Interpretability Analysis: Include interpretability analysis tools to understand the impact of the discovered components on the model's decision-making process, providing insights into the inner workings of the optimized models.