Comment by esafak
No, they are not. Model outputs can be discretized but the model parameters (excluding hyperparameters) are typically continuous. That's why we can use gradient descent.
No, they are not. Model outputs can be discretized but the model parameters (excluding hyperparameters) are typically continuous. That's why we can use gradient descent.
I agree if you approach it naively you will accomplish nothing.
With some optimization, you can evolve programs with search spaces of 10^10000 states (i.e., 10 unique instructions, 10000 instructions long) and beyond.
Visiting every possible combination is not the goal here.
Where are the model parameters stored and how are they represented?