Fig. 1
From: Hybrid evolutionary-gradient training improves long-term time series forecasting
Overview of EGMF-GR. A population of architecture matched individuals is maintained with weight diversity. Training integrates population based exploration with gradient based refinement by selecting a global best individual, generating an offspring via module level state transfer, and refining the offspring with a short backpropagation stage. Module transfer is applied only to aligned modules and is triggered by a robust threshold on multi metric discrepancies. When transfer is activated, learnable parameters are updated and non learnable buffers are synchronized when present to keep the model state consistent.
