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

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