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Quality-diversity in dissimilarity spaces

arXiv 2211.12337 ↗First posted 14 Nov 2022cs.AI cs.NE math.OCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 172 references that are in the map. First posted on arXiv 14 Nov 2022.

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25 of 172 references.

References · 25

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  12. 2021Geometric Entropic Explorationcited by 32
13 more
  1. 2019Self-Attention Based Molecule Representation for Predicting Drug-Target Interactioncited by 22
  2. 2018Optimization of the Sherrington-Kirkpatrick Hamiltoniancited by 20
  3. 2020BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity searchcited by 15
  4. 2019Benchmarking Discrete Optimization Heuristics with IOHprofilercited by 14
  5. 2020Multi-Emitter MAP-Elites: Improving quality, diversity and convergence speed with heterogeneous sets of emitterscited by 14
  6. 2015Low Autocorrelation Binary Sequencescited by 10
  7. 2021Weighting vectors for machine learning: numerical harmonic analysis applied to boundary detectioncited by 6
  8. 2020A Scenario-Based Development Framework for Autonomous Drivingcited by 2
  9. 2022Diversity Enhancement via Magnitudecited by 2
  10. 2022Parallel black-box optimization of expensive high-dimensional multimodal functions via magnitudecited by 2
  11. 2017An N log N Parallel Fast Direct Solver for Kernel Matricescited by 1
  12. 2019Comparing reliability of grid-based Quality-Diversity algorithms using artificial landscapescited by 1
  13. 2020Practical applications of metric space magnitude and weighting vectorscited by 1

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