Evolving Neural Networks for Bi-Level Human Decision-Making Prediction in Mineral Processing

This paper presents a novel approach for predicting bi-level human decision-making in mineral processing. This method aims to enhance the adaptability of human decision-making support systems and reduce dependence on expert knowledge. To achieve this, it leverages a combination of evolutionary computation and neural networks. The proposed method utilizes a hierarchical neural network framework optimized through evolutionary computation. This framework takes advantage of the sequential nature of decision-making within the mineral processing system. The effectiveness of the method is validated through experiments using 1,324 real-world data entries from a mineral processing plant in China. The results demonstrate the method’s capability to improve prediction accuracy.