pyribs: A Bare-Bones Python Library for Quality Diversity Optimization
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 repr...
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Main Authors | , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
28.02.2023
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2303.00191 |