Surrogate Assisted Evolutionary Algorithm

Surrogate-assisted evolutionary algorithms (SAEAs) aim to efficiently solve computationally expensive optimization problems by replacing the true objective function with a cheaper-to-evaluate surrogate model, typically a machine learning model like Kriging, Random Forests, or neural networks (including specialized architectures like Kolmogorov-Arnold Networks). Current research focuses on improving surrogate model accuracy and efficiency, particularly for high-dimensional problems and multi-objective optimization, often incorporating techniques like knowledge transfer and handling model uncertainty. The impact of SAEAs is significant, enabling the optimization of complex real-world systems in fields like engineering design, materials science, and deep learning where traditional methods are computationally prohibitive.