Evolve

You don't find the best implementation by improving one. You find it by improving many and keeping the winners.

Why This Works

Hill climbing improves a single candidate and hopes it's in the right basin. Evolutionary search maintains a population — multiple candidates exploring different regions of the solution space simultaneously. Selection keeps the best, mutation explores nearby, crossover combines good ideas, and fresh random prevents the population from collapsing to a local optimum.

LLMs make vastly better mutation operators than random perturbation. They understand code semantics, so mutations are meaningful — not "flip a bit" but "swap the sorting algorithm" or "add a caching layer." This turns evolutionary search from brute force into intelligent exploration.

Relationship to Existing Skills