Evolutionary Pruning

Evolutionary pruning is a model compression technique that uses evolutionary algorithms to optimize the removal of less important connections or neurons in neural networks, aiming to reduce computational cost without significant performance loss. Current research focuses on applying this approach to various architectures, including convolutional neural networks (CNNs), vision transformers, and spiking neural networks (SNNs), often incorporating multi-objective optimization to balance accuracy, robustness, and compactness. These methods show promise in improving the efficiency and deployability of large models across diverse applications, particularly in resource-constrained environments, and are actively being refined to enhance both speed and accuracy.