Population-Based Novelty Searches Can Converge

Authors

  • R. Paul Wiegand Winthrop University

DOI:

https://doi.org/10.32473/flairs.v34i1.128753

Keywords:

novelty search, k nearest neighbor, packing, convergence

Abstract

Novelty search is a powerful tool for finding sets of complex objects in complicated, open-ended spaces. Recent empirical analysis on a simplified version of novelty search makes it clear that novelty search happens at the level of the archive space, not the individual point space. The sparseness measure and archive update criterion create a process that is driven by a clear pair of objectives: spread out to cover the space, while trying to remain as efficiently packed as possible driving these simplified variants to converge to an

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Published

2021-04-20

How to Cite

Wiegand, R. P. (2021). Population-Based Novelty Searches Can Converge. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128753

Issue

Section

Main Track Proceedings