The one I liked was the postgres genetic optimizer. https://www.postgresql.org/docs/17/geqo-pg-intro.html https://www.postgresql.org/docs/17/geqo-pg-intro.html

The one I liked was the postgres genetic optimizer. https://www.postgresql.org/docs/17/geqo-pg-intro.html https://www.postgresql.org/docs/17/geqo-pg-intro.html The theory being an exhaustive search of all possible query plans is np-hard and would take too long, so you do a limited, iterative, best fit search. My understanding is it never worked super great and would only be used if your query exceeded some high level of complexity. I distinctly remember it being removed at some point, but I see it mentioned in current docs, so I am probably wrong about that. Anyway I wonder if, with some of the new advances in machine learning, it would be worth revisiting this approach to optimization.