Sequential Optimization

Sequential optimization focuses on efficiently finding optimal solutions in a series of steps, leveraging information from previous iterations to guide subsequent searches. Current research emphasizes developing robust and scalable algorithms, particularly within Bayesian optimization frameworks using Gaussian processes or tree-based models, and adapting these to handle high-dimensional spaces, noisy data, and multi-fidelity evaluations. These advancements are crucial for tackling computationally expensive problems across diverse fields, including materials science, robotics, and machine learning model training, where efficient exploration of vast search spaces is paramount.