Propose an approach to hyperparameter tuning that addresses the challenges of overfitting when increasing representational capacity. Explain how you would leverage this understanding to improve generalization performance.

To address overfitting when increasing representational capacity, I would use techniques such as early stopping or learning rate schedules to prevent the model from overfitting to the training data. Additionally, I would consider using more effective regularization techniques, such as dropout or batch normalization, to reduce overfitting. Furthermore, I would plot the generalization error as a function of one of the hyperparameters to identify the optimal value and avoid the U-shaped curve.