6 citations · 10 across the 5 of their papers we have counts for
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cs.NE2025★ 6 cited
Overcoming Deceptiveness in Fitness Optimization with Unsupervised Quality-Diversity
Lisa Coiffard, Paul Templier, Antoine Cully
Policy optimization seeks the best solution to a control problem according to an objective or fitness function, serving as a fundamental field of engineering and research with appl…
cs.NE2025★ 1 cited
Dominated Novelty Search: Rethinking Local Competition in Quality-Diversity
Ryan Bahlous-Boldi, Maxence Faldor, Luca Grillotti +4
Quality-Diversity is a family of evolutionary algorithms that generate diverse, high-performing solutions through local competition principles inspired by natural evolution. While…