Adaptive Niching-Based Gradient-Accelerated Differential Evolution for High-Dimensional Nonconvex Optimization

This paper presents AdaptiveGDE, a new optimization algorithm designed to help train deep neural networks (DNNs, which are AI models with many layers) more effectively. The algorithm combines differential evolution (a method that mimics natural selection to find good solutions) with gradient descent (a technique that adjusts parameters to reduce errors), and uses an adaptive niching strategy (dynamically dividing the search into smaller groups) to balance exploring many possibilities early on with refining the best solutions later.