Results seem mostly noise to me. One eval per model, in a large problem space (i.e. a problem which requires many attempts to solve well).

If you're curious what the actual optimal Paris cost is, I suggest formulating the problem as an integer linear program and submitting it to Gurobi on NEOS [0]. Gurobi is arguably the strongest commercial ILP solver; big companies pay big dollars to use it to optimise schedules, industrial processes, etc. I'm not sure it could solve this problem to optimality in the 8 hours NEOS provides you, but it might -- KIRO has some similarities to the Vehicle Routing Problem, variants of which are very commercially important. In any case, Gurobi is a monster, and even if you don't get an exact solution, it will give you a lower bound (which may not be tight, but it's nonetheless interesting).

[0] https://neos-server.org/neos/

Just use OpenEvolve for such problems.

A deepdive on the /goal effect on a problem literally made for this.

I love that we have this on one hand and me cleaning up catastrophic CSS made by Sol on the other. Then again, maybe CSS is the ultimate benchmark.

Very interesting, will surely help my future projects

...is this not a Travelling Salesman Problem?

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