autotuningalgoritme
Autotuningalgoritme refers to algorithms designed to automatically adjust parameters within a system to achieve optimal performance. These algorithms are particularly prevalent in fields like digital signal processing, control systems, and database management. The core idea is to eliminate the need for manual tuning, which can be time-consuming, complex, and prone to human error. Autotuning algorithms typically explore a range of parameter values, evaluating the system's response at each step, and converging towards a configuration that best meets a predefined objective function. This objective function could represent metrics such as efficiency, accuracy, speed, or stability.
Common approaches within autotuning include gradient descent, genetic algorithms, Bayesian optimization, and reinforcement learning. For instance,