Differential evolution (DE) is a population-based metaheuristic designed to solve continuous global optimization problems with high efficiency and simplicity. Introduced in the mid-1990s, DE ...

Neural ensembles, comprising multiple heterogeneous neural networks, show promise in machine learning tasks. Their efficacy depends on architecture design, traditionally relying on deep learning ...