# pyribs: A Bare-Bones Python Library for Quality Diversity Optimization

Source: [pyribs: A Bare-Bones Python Library for Quality Diversity Optimization](<https://www.sota2.com/research/paper/68fa6f141e209131cea43bfb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:13:37.852Z

Authors: Bryon Tjanaka, Matthew C. Fontaine, David H. Lee, Yulun Zhang, Nivedit Reddy Balam, Nathaniel Dennler, Sujay S. Garlanka, Nikitas Dimitri Klapsis, Stefanos Nikolaidis

Published: 2023-03-01T02:48:55.000Z

## Abstract

Recent years have seen a rise in the popularity of quality diversity (QD) optimization, a branch of optimization that seeks to find a collection of diverse, high-performing solutions to a given problem. To grow further, we believe the QD community faces two challenges: developing a framework to represent the field's growing array of algorithms, and implementing that framework in software that supports a range of researchers and practitioners. To address these challenges, we have developed pyribs, a library built on a highly modular conceptual QD framework. By replacing components in the conceptual framework, and hence in pyribs, users can compose algorithms from across the QD literature; equally important, they can identify unexplored algorithm variations. Furthermore, pyribs makes this framework simple, flexible, and accessible, with a user-friendly API supported by extensive documentation and tutorials. This paper overviews the creation of pyribs, focusing on the conceptual framework that it implements and the design principles that have guided the library's development.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Quality-Diversity Optimization on LSI](<https://www.sota2.com/research/sota/quality-diversity-optimization-on-lsi.md>): Quality-Diversity Optimization; LSI; QD-score: 16.54.

- [Quality-Diversity Optimization on Arm Repertoire](<https://www.sota2.com/research/sota/quality-diversity-optimization-on-arm-repertoire.md>): Quality-Diversity Optimization; Arm Repertoire; QD-score: 61.59.

- [Quality Diversity on LSI StyleGAN+CLIP](<https://www.sota2.com/research/sota/quality-diversity-on-lsi-stylegan-plus-clip.md>): Quality Diversity; LSI StyleGAN+CLIP; QD-score: 16.54.

- [Quality-Diversity Optimization on LP sphere](<https://www.sota2.com/research/sota/quality-diversity-optimization-on-lp-sphere.md>): Quality-Diversity Optimization; LP sphere; QD-score: 34.74.

- [Quality-Diversity Optimization on LP Rastrigin](<https://www.sota2.com/research/sota/quality-diversity-optimization-on-lp-rastrigin.md>): Quality-Diversity Optimization; LP Rastrigin; QD-score: 22.65.

- [Quality Diversity on Linear Projection (Rastrigin) n=1000](<https://www.sota2.com/research/sota/quality-diversity-on-linear-projection-rastrigin-n-1000.md>): Quality Diversity; Linear Projection (Rastrigin) n=1000; QD-score: 8.12.

- [Quality Diversity on Linear Projection Sphere n=1000](<https://www.sota2.com/research/sota/quality-diversity-on-linear-projection-sphere-n-1000.md>): Quality Diversity; Linear Projection sphere n=1000; QD-score: 12.21.

- [Quality Diversity on Arm Repertoire 1000-DOF](<https://www.sota2.com/research/sota/quality-diversity-on-arm-repertoire-1000-dof.md>): Quality Diversity; Arm Repertoire 1000-DOF; QD-score: 33.51.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2303.00191>)

[PDF](<https://arxiv.org/pdf/2303.00191.pdf>)