[msarchet]

It’s the simulation and fitness function that are difficul not the genetic algorithm really.

I’ve done a bunch of playing around with NEAT, a variant of GA using NNs, for various things. Typically for GA stuff though you have a genome, aka some set of instructions for an individual, a fitness function for scoring them, and then you generate new individuals from those genomes for the next population.

Original Paper on NEAT here:

https://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf

Lots of good resources here.

https://nn.cs.utexas.edu/

[orthecreedence]

I love the NEAT algorithm. I did version of it for my senior project in high school, and have done a few iterations since, mostly with bugs that eat food and avoid predators. I'm about due for another round.