Fig. 2
From: Hybrid evolutionary-gradient training improves long-term time series forecasting
Pipeline of globally guided module level fusion. A current individual and the global best individual execute paired forward propagation under the same inputs, and aligned modules are monitored to collect intermediate outputs. For each module, a normalized multi-metric discrepancy score is computed from JSD, KLD, MSE, and MAE after normalization. A hybrid threshold based on the third quartile and a gamma scaled IQR decides whether fusion is triggered. When triggered, the fusion weight interpolates between the best and current module states, optionally with a small Gaussian perturbation, otherwise no change is applied. Module monitoring automatically matches architecture aligned trunk modules under a fixed exclusion rule for input embedding and final head, so the operator remains reproducible and not tied to a specific backbone family.
