Black-Box Optimization

Black-box optimization is a method used to find the best solution to a problem when the internal details of how options work are unknown or inaccessible. Instead, it treats the system as a "black box" that can be tested by trying different inputs and observing the outputs. Based on these results, it iteratively searches for the optimal input without understanding the internal mechanisms. This approach is useful in complex scenarios like tuning machine learning models or engineering designs where the internal processes are complex or hidden but evaluating outcomes is possible.