Thinking a little “out of the box” and deviating from your initial theory a bit, something that comes to my mind when I think about the problem of a universal encoder generation logic is that software could in theory be used to emulate how encoders (i.e. sensory organs) might change/improve in nature, using a system by which “genetics” can be modified and compete with the original and with other variations that have different modifications. There are three relevant theories/areas that might be considered:
- Survival of the fittest
- Selective breeding
- Genetic modification
These three are also listed from slowest to fastest, and presumably the reason for that is the level of involvement from an intelligence (a breeder or genetic engineer in this case). If you think of a software’s source code as its “genetics”, one or more of these three mechanisms might be emulated to develop encoders. In the case of the latter two, the “intelligence” would presumably be an AI.
Survival of the fittest
This would be the easiest to emulate. A system would need to be designed in which “creatures” would rely on the encoder in some way to survive and compete with other creatures. The competition would need to be tailored in a way that an ideal encoder would ensure the best survival rate for the creatures. The system would make random changes to the encoder’s source code and plug it onto the creatures to compete. This would likely take an extremely long time (probably outside the limits of human time-frames), because you would be relying on what is likely to be fairly complex logic to be generated purely by random chance. For complex pieces that cannot be evolved through a series of small enough steps, this could be equivalent to waiting on a monkey randomly typing on a keyboard to type out the Gettysburg Address. On the flip side, you do not require an intelligence to be involved – if you can wait long enough for it to just happen 
Selective Breeding
This would be similar to “survival of the fittest”, but with the introduction of an AI advisor that was intelligent enough to recognize patterns earlier than they would have been identified by the “survival of the fittest” strategy alone, and bias those genetics (even if they do not provide an immediate benefit to the creatures). The AI would presumably learn through watching numerous “survival of the fittest” competitions and over time learning to recognize patterns that lead to improved encoders. The AI would need the ability to select certain creatures and give them an advantage in the competition to ensure their genetics continue into subsequent generations. This would be something like spotting the monkey typing “Four score and seven bananasflkjd aoie ha…” and being able to preserve the first four words even though they, by themselves, are not the full Gettysburg Address.
Genetic Modification
In this case, you would need an even more intelligent AI, which has the ability to view the source code and make modifications to it. This would be equivalent to replacing the monkey with Abraham Lincoln (i.e. no longer relying on randomness). There was an interesting conversation about teaching an AI to code on this thread. I think many projects like that would need to happen first, to explore how to make an AI sophisticated enough to correlate source code with solutions to problems. This is obviously not something which could be done today, but certainly feasible as AIs become more sophisticated in the future.