Does Dimensionality Reduction via Random Projections preserve Landscape Features?
Iván Olarte Rodríguez, Anja Jankovič, Thomas Bäck, Elena Raponi · Proceedings of the Genetic and Evolutionary Computation Conference · 2026
Exploratory Landscape Analysis (ELA) provides numerical features for characterizing black-box optimization problems. In high-dimensional settings, however, ELA suffers from sparsity effects, high estimator variance, and the prohibitive cost of computing several feature classes. Dimensionality reduction has therefore been proposed as a way to make ELA applicable in such settings, but it remains unclear whether features computed in reduced spaces still reflect intrinsic properties of the original landscape.