Adaptive Learning in Non-Stationary Computer Science Environments with Self-Evolving AI Systems

The uses of Modern Computer Science and Engineering (CSE) such as cybersecurity monitoring, IoT analytics, software log analysis are highly non-stationary and their changing data distributions introduce concept drift and rapidly worsen the performance of fixed machine learning models. This paper proposes a solution to such a challenge in the form of Self-Evolving Artificial Intelligence (SEAI) framework, where concept drift detect, meta-learning, continual learning, self-supervising representation learning, and adaptive transformer architecture are combined into one online framework. SEAI recognizes changes in distributional changes automatically and builds its model fast through meta-learning initializing and overcomes the problem of catastrophic forgetting through continual learning regularization and constrained replay. The framework is also tested with the help of CICIDs-2021 data on cybersecurity, the IoT-23 data on streaming traffic and altering software logs data. The experiment findings indicate that the drift-detecting precision is 91.4, adaptation latency is 0.7 seconds, and forgetting is much lower than in the scenario of stable transformers and replaybased continual learning baselines, without disastrously high memory consumption. The findings substantiate the fact that SEAI is useful in offering robust and reliable adaptive intelligence on non-stationary real world CSE environments.