Deep neural networks (DNNs), which are extensions of artificial neural networks, can learn
higher levels of feature hierarchy established by lower level features by transforming the raw …

Integrating deep learning methods into metaheuristic algorithms has gained attention for
addressing design-related issues and enhancing performance. The primary objective is to …

This study provides an overview of the literature on automated adaptation of machine
learning models via metaheuristics, in settings with concept drift. Drift-adaptation of machine …

This paper proposed to use the Biased Random-Key Genetic Algorithm (BRKGA) to optimize
the main hyperparameters of a Deep Feedforward Neural Network (DFNN). The proposed …

The robustness and resilience of the deep learning models offer consistent and competitive
results in real-world applications. Despite its adaptability, the training and adjustment of the …

Due to the lack of consideration of movement behavior information other than time and
location perception in current location prediction methods, the movement characteristics of …