I read about the on_generation parameter, which allows to write a user defined function. This is a very useful method and underlines conceptual aspects of pygad. Stopping based on a rule, skipping remaining generations, is possible through this.

But I would think of two ways to implement exit criteria. A) Some examples in the documentation or B) maybe beyond some presented logic some internal criteria.

exit_criteria=None
All generations are executed (default), the user might want to exit on his own desire using on_generation (function)

exit_criteria="seen_20"
The execution would stop after 20% of all combinatorical (precision) gene variations have been seen/tested

exit_criteria="stale_10"
The execution would stop after 10 generations with no new best solution found

exit_criteria="flatmox_1_5"
The execution would stop if the increase in fitness is below 1% for the last 5 generations (flattening approximation max)

Just some thoughts. But I think that some criteria better describe where one is aiming for. Sometimes it is very hard to estimate needed generations. Would be neat to have. Otherwise it might direct others on their search for something like this.