Hello kupferstecher,
In this message, I'll try to explore creativity.
Byte code is the way you have to program a X86 processor as example. Weights are the way to "code" a neural network. Therefore, byte code to X86 is equivalent to weights to a Neural Network (NN). The NN is your processor in this example.
In supervised learning, the NN learns the code itself given inputs and expected outputs. In the case that you want to stick with boolean algebra, you can easily have NN layers representing logic ports OR, AND, NOT, NAND,... It's quick to perceive that you can encode any boolean logic into NN. If you get an FPGA, you can make neurons to work in parallel...
Going back to your question, if you really want asm code as output, I would suggest you to use an interpreter associated with evolutionary computing to create the best asm code via reproduction and selection of the best fit. This approach might work for small problems such as "what is the best (or just a very good) dot product algorithm". I have an example with evolutionary algorithm to create magic squares.