A genetic algorithm (artificial intelligence) that learns, through various genetic evolution generations, to move towards a point, avoiding obstacles.

The idea is that every car has a DNA, which dictates how to will move and the steps it will take. Their fitness function, aka how close they got to the goal, will decide whether they reproduce or die. If they reproduce, the DNA of the child will be slightly altered. According to darwinist theory, "survival of the fittest" should mean that eventually, only cars that reach the goal are left.

Ofcourse, there are very large limitations. For example, if the obstacles blocking the goal are too hard to "guess" the way around after a reasonable number of iterations, the cars will never reach the obstacle. To fix this, the cars would need an actual brain that learns how to "get around obstacles" in general, instead of how to "get around those specific obstacles that the cars were trained under". Nevertheless, this was my first attempt at a self-learning computer so I will make another better one soon.

Developed by Sebi, Jorge