Artificial Intelligence #residual-space#evolutionary optimization
Residual-Space Evolutionary Optimization via Flow-based Generative Models
A new framework called residual-space evolutionary optimization addresses the challenge of data editing with non-differentiable objectives in flow-based generative models. By operating in residual space, it separates local exploitation (self-pollination) from broader exploration (cross-pollination). The method was validated on the MorphoMNIST benchmark and crystal data, showing balanced target alignment, instance preservation, and diversity.
Jun 22, 2026 1 source