Open AccessPopulation-Based Novelty Searches Can Converge
Author(s) -
R. Paul Wiegand
Publication year - 2021
Publication title -
proceedings of the ... international florida artificial intelligence research society conference
Language(s) - English
Resource type - Journals
eISSN - 2334-0762
pISSN - 2334-0754
DOI - 10.32473/flairs.v34i1.128753
Subject(s) - novelty , space (punctuation) , computer science , measure (data warehouse) , cover (algebra) , population , process (computing) , point (geometry) , information retrieval , artificial intelligence , data mining , theoretical computer science , machine learning , mathematics , engineering , mechanical engineering , philosophy , demography , theology , geometry , sociology , operating system
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