Evolving Two Sum: From O(n²) to O(n) with OpenEvolve

[chunhualiao]

I spent today experimenting with OpenEvolve (the open-source counterpart to DeepMind’s AlphaEvolve) on a RHEL 9 laptop using the OpenAI API. To get it running, I fixed a small hashing issue—swapping hashlib.md5 for hashlib.sha256 due to Red Hat constraints—and added support for gpt-5, modeling it after the other o-series entries.

For a test task, I used the classic Two Sum Python problem (scan a list and find two numbers that add up to a target). With the default settings, OpenEvolve failed to reach the optimal solution and declared the O(n²) brute-force approach as the best.

Digging into the logs (and checking with AI) revealed that evaluator.py sets the optimization objective. The defaults overweight correctness and rely on overly simple tests. I expanded the test suite with edge cases and larger inputs, and rebalanced the objective to 50/50 for correctness and efficiency (instead of 70/30). Last but not least, I enabled full rewrite (not the default diff-based evolution).

After rerunning, OpenEvolve discovered the optimal O(n) solution in a few iterations. My takeaway: the hard part is crafting the evaluator—the tests and weightings heavily steer outcomes. I also noticed it starts from an initial program rather than generating from scratch, which I hadn’t expected.

Overall, in 1–2 hours I got OpenEvolve working and producing useful results. I’m pleased with the progress and plan to keep exploring; it’s a very promising open-source project.

More details are here

• https://gist.github.com/chunhualiao/571dd392121b6f7439cdab742063134c

[codelion]

Hey this is great work, you can add your example in the repo, it will help people look at how to craft the evaluator.py properly since that usually takes most of the time as you pointed out.