Which combination of 3 hyperparameters to combat overfitting of a convolutional neural network?
If you are asking specifically about overfitting, I would only keep dropout rate from your list of three. Other two can be any chosen from: number of filters in the convolutional layers, number of convolutions, number of dense layers (if any), number of neurons in dense layers. 

Learning rate should be optimized but not for the purpose of combatting overfitting, at least the way I understand it. Using Keras, you can start with some learning rate - not necessarily very fine tuned, and slowly reduce it over time once the training reaches a plateau using Learning Rate Scheduler in Callbacks. Also, there are ways to find an optimal learning rate such as learning rate finder, so it would probably be a misuse of resources to optimize for learning rate using grid search (I never used them though!).

You can also use the Model Checkpoint present in the above Callbacks page to save the model as the validation loss improves only, and ignore the later epochs where overfitting becomes an issue. This way, you won't need to include number of epochs in your search.