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cs.NE2026
Tournament Informed Adversarial Quality Diversity
Timothée Anne, Noah Syrkis, Meriem Elhosni +5
Quality diversity (QD) is a branch of evolutionary computation that seeks high-quality and behaviorally diverse solutions to a problem. While adversarial problems are common, class…
cs.NE2026
Adversarial Coevolutionary Illumination with Generational Adversarial MAP-Elites
Timothée Anne, Noah Syrkis, Meriem Elhosni +4
Quality-Diversity (QD) algorithms seek to discover diverse, high-performing solutions across a behavior space, in contrast to conventional optimization methods that target a single…
cs.NE2025
Hypernetworks That Evolve Themselves
Joachim Winther Pedersen, Erwan Plantec, Eleni Nisioti +4
How can neural networks evolve themselves without relying on external optimizers? We propose Self-Referential Graph HyperNetworks, systems where the very machinery of variation and…