👉 Adoptive computing, also known as adaptive computing or self-tuning systems, is a paradigm where software or hardware components dynamically adjust their behavior and parameters to optimize performance based on the current workload or environmental conditions. This approach allows systems to learn from their interactions and adapt in real-time, improving efficiency, responsiveness, and resource utilization without requiring explicit programming for every possible scenario. For instance, in machine learning, adoptive computing enables models to fine-tune their parameters as they encounter new data, enhancing accuracy and adaptability. Similarly, in networking, it can optimize routing protocols to handle varying traffic loads more effectively. By continuously adapting, adoptive computing systems can offer robust performance across diverse and unpredictable environments.
