# EmergentDB Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/map-elites-evolutionary-algorithm-index-types-hnsw
> Markdown URL: https://aitinkerers.org/technologies/map-elites-evolutionary-algorithm-index-types-hnsw.md
> Technology record last updated: 2026-02-24T05:28:09Z
> Generated: 2026-10-25T13:48:20Z

A self-optimizing vector database architecture that uses the MAP-Elites quality-diversity algorithm to evolve and select high-performance HNSW index configurations.

EmergentDB replaces manual index tuning with an evolutionary loop called IndexQD, which utilizes the MAP-Elites algorithm to navigate the trade-offs between recall, latency, and memory. By treating the database structure as a phenotype, the system mutates HNSW hyperparameters—specifically M, ef_construction, and ef_search—to populate a multi-dimensional archive of elite configurations. This approach allows the engine to automatically discover optimal graph structures for specific datasets, achieving up to 100% recall and significantly higher throughput than static implementations like ChromaDB or LanceDB by continuously illuminating the most efficient regions of the index parameter space.

- Official technology site: https://emergentdb.com
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [EmergentDB: How Evolutionary Algorithms Made My Vector Database 82x Faster](https://singapore.aitinkerers.org/talks/rsvp_hHLd9-vXn8A)

- Event context: AI Tinkerers - The Age of AI & Infrastructure (Singapore) — 2026-02-11 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_hHLd9-vXn8A

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