Evolutionary Intelligence: A Hybrid Neural Framework for Dynamic System Innovation

The Adaptive Neural Evolutionary Framework for Building Dynamic Systems with Hybrid Intelligent Architectures is designed to address challenges in real-time adaptability, optimization, and performance of complex systems. This framework integrates Neural Networks (NNs), such as Long Short-Term Memory (LSTM) and Residual Networks (ResNet), with Evolutionary Algorithms (EAs), including Genetic Algorithms, to dynamically optimize hyperparameters and structural configurations. Preprocessing techniques like Wavelet Transform and Adaptive Contrast Enhancement (ACE) enhance data clarity and signal quality, significantly improving system input. The proposed hybrid framework achieves superior accuracy compared to traditional models, with a training accuracy of 96.8%, validation accuracy of 95.5%, and a testing accuracy of 96.9%. Experimental results demonstrate that the system performs exceptionally well under dynamic scenarios such as sudden input shifts, unseen data distributions, and resource constraints. Furthermore, the integration of adaptive feedback loops and neural plasticity ensures continual learning and model evolution in real-time. The framework's robustness and adaptability make it suitable for applications in predictive modeling, decision-making systems, and dynamic environments.