aiqbench takeMachine Learning

This paper advances quality-diversity optimization by applying the theory of magnitude to generic dissimilarity spaces, offering a generalized framework for Go-Explore algorithms. The work contributes directly to machine learning research by providing mathematical rigor for maximizing solution diversity in complex search landscapes.

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Quality-diversity in dissimilarity spaces

arXivSeptember 7, 20261 min read

arXiv:2211.12337v4 Announce Type: replace Abstract: The theory of magnitude provides a mathematical framework for quantifying and maximizing diversity. We apply this framework to formulate quality-diversity algorithms in generic dissimilarity spaces. In particular, we instantiate and demonstrate a very general version of Go-Explore with promising performance.

Abstract:The theory of magnitude provides a mathematical framework for quantifying and maximizing diversity. We apply this framework to formulate quality-diversity algorithms in generic dissimilarity spaces. In particular, we instantiate and demonstrate a very general version of Go-Explore with promising performance.

Patched Section 7 (not in the GECCO 2023 version at DOI https://doi.org/10.1145/3583131.3590409) with inline forward reference to https://arxiv.org/html/2509.19565 and this https URL, which contain the correct algorithm and proofs. No experimental results are materially affected in either this version or the GECCO one

Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Optimization and Control (math.OC)

arXiv:2211.12337 [cs.AI]

(or arXiv:2211.12337v4 [cs.AI] for this version)

https://doi.org/10.1145/3583131.3590409 DOI(s) linking to related resources

Full text under the publisher's open licence · 109 words

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