# Darwinian Data Structure Selection

arXiv: https://arxiv.org/abs/1706.03232 · HTML: https://mlsanity.com/paper/1706.03232/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2017.

## Built on by (most cited first, 3 of 3)

- [Evolving Excellence: Automated Optimization of LLM-based Agents](https://mlsanity.com/paper/2512.09108/) (2025)
- [MAGPIE: Machine Automated General Performance Improvement via Evolution of Software](https://mlsanity.com/paper/2208.02811/) (2022)
- [evoML Yellow Paper: Evolutionary AI and Optimisation Studio](https://mlsanity.com/paper/2212.10671/) (2022)

## Abstract (arXiv)

Data structure selection and tuning is laborious but can vastly improve an application's performance and memory footprint. Some data structures share a common interface and enjoy multiple implementations. We call them Darwinian Data Structures (DDS), since we can subject their implementations to survival of the fittest. We introduce ARTEMIS a multi-objective, cloud-based search-based optimisation framework that automatically finds optimal, tuned DDS modulo a test suite, then changes an application to use that DDS. ARTEMIS achieves substantial performance improvements for every project in 5 Java projects from DaCapo benchmark, 8 popular projects and 30 uniformly sampled projects from GitHub. For execution time, CPU usage, and memory consumption, ARTEMIS finds at least one solution that improves all measures for 86% (37/43) of the projects. The median improvement across the best solutions is 4.8%, 10.1%, 5.1% for runtime, memory and CPU usage. These aggregate results understate ARTEMIS's potential impact. Some of the benchmarks it improves are libraries or utility functions. Two examples are gson, a ubiquitous Java serialization framework, and xalan, Apache's XML transformation tool. ARTEMIS improves gson by 16.5%, 1% and 2.2% for memory, runtime, and CPU; ARTEMIS improves xalan's memory consumption by 23.5%. Every client of these projects will benefit from these performance improvements.