What is new population in genetic algorithm?
In the simplest implementations, you simply discard the old population and maintain a population of size $n$ in each generation.

There is lots of variation on this though, any of which could be useful in practice. As a start to show the range of possibilities, you can do some or all of the following:


Keep a top number (say $m$) of the original population, and only generate $n-m$ new offspring in each generation.
Generate offspring in small groups and assess them. They must beat a member of the current population on the fitness measure in order to find a place (this can work nicely in round-robin tournaments, where the fitness measure is winner of a game).
Keep snapshots every so many generations (e.g. every 10 generations) of the whole population or just the best performing individuals. You may use these snapshots to assess long-term improvement or as a "gene pool" to keep good performers around in some form for a while longer than a single generation.


Variations like these are hyperparameters for your GA. They might help in certain circumstances, or be a hindrance in others. So start with the simple "everything is replaced" is recommended initially, especially if you are learning genetic algorithms.