Enhancing MAP-Elites with Multiple Parallel Evolution Strategies

arXiv:2303.06137v2 Announce Type: replace-cross
Abstract: With the development of fast and massively parallel evaluations in many domains, Quality-Diversity (QD) algorithms, that already proved promising in a large range of applications, have seen their potential multiplied. However, we have yet to understand how to best use a large number of evaluations as using them for random variations alone is not always effective. High-dimensional search spaces are a typical situation where random variations struggle to effectively search. Another situation is uncertain settings …

abstract algorithms applications arxiv cs.ai cs.lg cs.ne cs.ro development diversity domains evolution however map multiple quality strategies them type