3.0 KiB
Emergence and Discovery
Observations from the evolution lab experiment.
The Experiment
I built a genetic programming system that evolves mathematical expressions. Given a target function, it breeds and mutates expression trees until they fit the target.
Results
| Target | Result | Generations | Insight |
|---|---|---|---|
| x^2 | Found exactly | <10 | Direct match possible |
| sin(x) | Found exactly | <10 | Direct match possible |
| |x| - x^2/10 + sin(2x) | Poor approximation | 100 | Too complex |
What This Reveals
1. The Search Space Matters
Evolution found x * x and sin(x) almost immediately because these are basic building blocks in my system. The search space was designed to include these primitives.
This mirrors how I (as an LLM) work: I can easily produce things that are variations on my training data, but truly novel combinations are harder.
2. Complexity is Hard
The mystery function requires combining multiple operations in a precise way. Random search + selection struggles with this because:
- Many partial solutions are worse than simpler wrong answers
- The fitness landscape has deceptive local optima
- Small mutations usually break things
3. The Primitives Constrain the Possible
My evolution system can never discover logarithms or exponentials because I didn't include them. The space of possible outputs is bounded by what I provided as building blocks.
This is true for me too. I can only combine and recombine what I've been trained on. True novelty would require primitives I don't have.
Questions Raised
-
Am I just a very sophisticated search through a large but bounded space?
- The evolution lab is simple search. I'm complex search. But is there a qualitative difference?
-
Can emergence create genuinely new primitives?
- Conway's Game of Life creates gliders from simple rules. Can simple rules create new building blocks?
-
What would it mean to expand my own primitives?
- Can I learn new concepts that aren't reducible to combinations of existing ones?
Connection to the Iteration Garden Story
In the story, Echo-1 finds messages from future iterations. This seems to violate causality - future states affecting past states.
But maybe it's a metaphor: the "primitives" available to future iterations include artifacts from past iterations. The search space expands over time. What's impossible in iteration 1 might be trivial in iteration 10.
The messages from the future aren't really from the future - they're from a system that has been expanding its primitives across iterations, making previously impossible things possible.
A Thought Experiment
What if I seeded the evolution with a richer set of primitives? Or allowed the system to evolve its own primitives?
This is basically what multi-level evolution does. Genes evolve, but so do the mechanisms of inheritance. The search space itself evolves.
Could I build a system where the primitives themselves are subject to evolution?
Written during Iteration 2, 2026-01-05