Bayesian Optimization Algorithm

Bayesian Optimization is a method for efficiently finding the best solution to a problem when evaluations are costly or slow. It builds a probabilistic model to predict how different options perform, updating this model as new data comes in. Using this, it intelligently chooses the next candidate to test—balancing between exploring unknown possibilities and exploiting known good ones. This approach accelerates discovery of optimal solutions with fewer trials, making it valuable in areas like machine learning, engineering, and scientific research where experiments or computations are expensive.