Optimization Process

Optimization processes aim to find the best solution from a vast search space, often involving computationally expensive evaluations. Current research focuses on improving efficiency through surrogate models (e.g., regression and pairwise models), Bayesian optimization techniques that incorporate noise and cost considerations, and the application of diverse algorithms like reinforcement learning, genetic algorithms, and neural networks (including physics-informed and recurrent architectures). These advancements are impacting diverse fields, from materials science and robotics to automated machine learning and telecommunications network design, by enabling faster, more efficient, and more robust solutions to complex problems.