Open Box Optimisation is a technique used in computer science and engineering to optimize a system by adjusting its parameters in a systematic and efficient manner. It is often applied in fields such as artificial intelligence, machine learning, and data mining.
Open Box Optimisation works by iteratively adjusting the parameters of a system and evaluating the resulting performance. This process is repeated until the optimal combination of parameters is found.
Open Box Optimisation allows for an efficient and systematic approach to optimization, saving time and resources compared to traditional trial-and-error methods. It also ensures that the optimal solution is found, rather than settling for a sub-optimal one.
Open Box Optimisation can be applied to a wide range of problems, such as feature selection, parameter tuning, and model selection. It is particularly useful for complex systems with many parameters that need to be optimized.
Like any optimization technique, Open Box Optimisation has its limitations. It requires a well-defined problem and a measurable objective function. It may also struggle with high-dimensional problems or those with non-linear relationships between parameters and performance.