Landscape Analysis

Landscape analysis aims to characterize the properties of optimization problems and data streams by analyzing their underlying structure and features, ultimately improving algorithm design and performance prediction. Current research focuses on developing novel methods for extracting informative features, including the use of deep learning architectures like transformers and variational autoencoders, and applying these features to diverse applications such as algorithm selection, surrogate modeling, and automated algorithm configuration. These advancements are significant because they enable more efficient and effective optimization strategies across various fields, from machine learning and engineering to ecology and environmental monitoring.