Expected Improvement

Expected Improvement (EI) is a key acquisition function in Bayesian Optimization (BO), a powerful technique for efficiently optimizing expensive-to-evaluate functions by balancing exploration and exploitation. Current research focuses on improving EI's performance, particularly by addressing its limitations in handling diverse optima, noisy environments, and cumulative regret, leading to the development of variants like Diverse EI and Self-Adjusting Weighted EI. These advancements enhance BO's applicability across diverse fields, including hyperparameter tuning, robotics, and engineering design, by enabling more robust and efficient optimization strategies.