Evolutionary Algorithms for Deep Learning in Secure Network Environments

The rapid advancements in deep learning and network security have necessitated the development of efficient algorithms to optimize model performance. In this research, we introduce a holistic approach to enhance deep learning models using evolutionary algorithms, specifically tailored for secure network environments. Central to our methodology are three innovative algorithms: Genetic Hyperparameter Optimization (GHPO), Coevolutionary Neural Architecture Search (CoNAS), and Transfer Learning with Genetic Adaptation (TLGA). The GHPO algorithm employs genetic algorithms to optimize hyperparameters, ensuring model robustness. CoNAS, on the other hand, dynamically evolves neural network architectures alongside adversarial strategies, ensuring models remain resilient against emerging threats. Lastly, TLGA leverages transfer learning in conjunction with genetic algorithms to fine-tune pre-trained models for specific security tasks. Each algorithm is grounded in rigorous mathematical foundations, and our results indicate their efficacy in optimizing model performance across various metrics, including Accuracy, Precision, Recall, F1 Score, and AUC-ROC. This research provides a comprehensive toolkit for researchers and practitioners, emphasizing the potential of evolutionary algorithms in optimizing deep learning models for network security.