Traveling Salesman!
I just finished a prototype for what I hope will be a good “fun Java lab” for my students. It’s an implementation of the Traveling Salesman Problem, as “solved” by genetic algorithms. I have it running on a map of 200 locations right now, and it is slowly but surely progressing in the right direction. That’s always encouraging!
It sure beats my artificial-ant simulation where the ants went away from the food! That one still needs some work…
With N locations, there are (N - 1)! / 2 different paths that can be chosen for the salesman to follow. So, for my 200 locations, there are around 2×10^372 different possibilities to be considered. The GAs search a whole lot less options than that (understatement), and they almost certainly won’t find the optimal solution, but at least you can watch them get better over time!
I have half a mind to leave this running overnight, but I’m not sure if my implementation can even cover the search-space completely. There might be a bug that prohibits it from fully exploring the search-space. But, I’ll let it go - see what I find in the morning!