So my problem is Number 5.
Okay tell me if I'm wrong but your problem is that you want to take two parent networks, and combine them to form a child network as some sort of simulation of sexual reproduction? How you do this depends entirely on what kind of neural network you're using.
If you're using a fixed topology (meaning the graph-structure), that never changes and thus every one of the creatures neural networks is the same then it's super easy to get the child's neural network (or geneome). All you have to do is run through the child's genome and select genes for it's father or mother randomly to fill the same location the gene would have filled in the parent, like
Father: A, B, C, D
Mother: 1, 2, 3, 4
Child: A, 2, 3, D or 1, B, C,4 or any random configuration.
If the topology of the neural network evolves along with the weights of the network then things are a little more difficult. You would need to use what is called the NEAT algorithm, it's a little tedious to read up but if you just google it you should find pdf documents explaining it. I would suggest just sticking with fixed topologies. I don't know Java so I can't actually help you out with code, but I could explain algorithms maybe in more detail.