What does a distributed genetic algorithm gain when the search landscape is small, rugged and deceptive? I evolved turmites with an asynchronous island-model GA over TCP in Rust, logged every generation and published the experiment and the paper.
The question that opened this project fits in one line: what does distribution buy when the search landscape is small, rugged and deceptive?
To answer it, with the help of AI agents I wrote a Rust algorithm that measures the capability of distributed evolutionary search over generalized Langton ants — turmites. The genetic algorithm runs as asynchronous islands communicating over TCP, records every generation without exception, and the repository ships both the experiment suite and the paper behind the numbers:
https://github.com/sudo-giusppe/langton-ga
Why turmites #
I already wrote about Langton’s ant here: two rules, two colors, and a highway that emerges on its own. The turmite is the natural generalization: more states, more colors, and a transition table that works as a genome. Evolving turmites means picking transition tables that produce interesting behavior — and that search landscape has a reputation for being exactly what you don’t want: small-looking, full of isolated peaks, and with optima that pretend to be more than they are.
That makes it a good test bench for a question about method: when the problem is this tricky, does splitting the population into islands that evolve on their own and occasionally swap individuals buy something real, or does it only buy pretty theory?
What’s in the box #
- A turmite simulator in Rust, with the genome and the evaluation separated from everything else.
- An island-model genetic algorithm, asynchronous : each island evolves at its own pace and talks to the others over TCP.
- Full generation logging : every generation is recorded, not just the final result.
- The experiment suite and thepaper backing the numbers, all in the same repository.
What the numbers say — and what they don’t #
I’m going to be honest with myself here: the current research on the topic is quite limited. What exists are exact paired permutation tests, all five seeds, and effect directions.
| Piece | Status |
|---|---|
| Exact paired permutation test | yes |
| All five seeds | yes |
| Effect direction | yes |
| Magnitude with intervals | not yet |
| Pre-registered confirmation | not yet |
For a first snapshot, that level of rigor is appropriate. For a confirmatory claim of effectiveness, it doesn’t reach. And I’d rather say it that way before someone else says it for me. Note. This is a snapshot, not a verdict. The numbers in the paper are what five seeds can support — no more, no less.
Core separated from experiments #
The most important design decision I made before writing the first line: the evolutionary core is deliberately separated from the experiments. Studying something new does not require touching the simulator or the genome.
With the current interface, without rewriting anything in the core, you can implement for example:
- Adaptive migration : decide when and how much to migrate based on the state of the islands, instead of a fixed timer.
- Topology : ring vs cellular vs gossip — who listens to whom, and how much it matters.
- Statistical rigor : more seeds, intervals, corrections for multiple comparisons.
- Multi-host execution : spread islands across several machines, which is the point of having TCP as the transport already.
Et cetera. The list is not closed; that’s the idea.
Closing #
Every generation is logged. If a number in the paper bothers you, you can pull the raw data from the repository and audit it yourself. To me, that’s the difference between an experiment and a demonstration.
The code, the experiments and the paper live here: