Research Article
Ashutosh kumar singh
Journal
International Journal of Digital Applications and Contemporary Research (IJDACR)
ISSN
2319-4863
Volume / Issue
Vol.14 · Issue 3
Published
October 2025
Access
Open Access
Licence
CC BY-NC-SA 4.0
Artificial Intelligence (AI) systems are being instituted in dynamic, uncertain, and safety-critical systems like financial analytics, healthcare allotment, intelligent traffic, cybersecurity and cyber-physical infrastructures. In these directions, traditional machine learning models with fixed training and accuracy-focused goals tend to fail in supporting performance in changing data distributions, rare events, and uncertainty and adversarial cases. To address these drawbacks, more advanced studies have highlighted optimization based-learning, generative-assisted models as well as adaptive AI designs that can evolve continuously, but can be reliable and ethical. The following review provides a synthesis of the state of optimization-enhancement and generative-assistance AI frameworks intended to work on the increase of the robustness, flexibility, and stability of the decisions taken. The paper focuses on anomaly-conscious learning pipelines, evolutionary and heuristic optimization methods, generative data enhancement and stress testing, adaptive signal processing, and governance conscious computer-aided intelligence. The applications in the fields of finance, health care, intelligent networks, and cyber-physical systems are surveyed in order to find common design principles and research issues. The end of the review is the projection of the future swinging on scalable, self-adaptive and ethically controlled AI systems.
Ashutosh kumar singh (2025). Optimization-Driven and Generative-Assisted Adaptive Artificial Intelligence Systems: A Detailed Review of Methods, Architectures, and Governance. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.14, Issue 3. ISSN: 2319-4863.
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