1. + 2. The distance to reach 100% fitness is set by me pretty arbitrarily. I just decided on distances that would be quite hard to reach with most creature designs (that aren't glitched out). So the fitness is just the percentage of this arbitrary distance that your creature managed to travel in the given time (where the max distance is also scaled by how much time you give the creature - so if you give them twice as much time, they would need to travel twice as far to get the same fitness score).
The selection probability is proportional to the fitness (Fitness Proportionate Selection - Wikipedia). So if you have 4 creatures that all reach a 1% fitness score, they are each going to have a 25% chance of being selected for each necessary recombination step.
3. The brain is a simple feed-forward neural network. Each node takes all of its inputs, multiplies each one with a weight (the weights are the things that are optimized during the simulation and they determine the behaviour), adds them all together and runs that result through a so-called activation function (1 / (1 + exp(-x)) which calculates the output of this particular node. 3Blue1Brown (on YouTube) made some really good videos on Neural Networks recently, so I'd definitely recommend to check them out. The whole topic is pretty math-heavy though.
4. I've definitely seen better results that way than having it the other way around (e.g. 5-20-100) but I've seen even better results with a more balanced node distribution. You're right, there are lots of deep neural networks, for example for pattern recognition, that are setup with decreasing weights per layer but in this case that doesn't seem to be the optimal solution. Maybe it can actually produce better results but only after a very long simulation.