  Although exploratory landscape analysis (ELA) has shown its effectiveness in
various applications, most previous studies focused only on low- and
moderate-dimensional problems. Thus, little is known about the scalability of
the ELA approach for large-scale optimization. In this context, first, this
paper analyzes the computational cost of features in the flacco package. Our
results reveal that two important feature classes (ela_level and ela_meta)
cannot be applied to large-scale optimization due to their high computational
cost. To improve the scalability of the ELA approach, this paper proposes a
dimensionality reduction framework that computes features in a reduced
lower-dimensional space than the original solution space. We demonstrate that
the proposed framework can drastically reduce the computation time of ela_level
and ela_meta for large dimensions. In addition, the proposed framework can make
the cell-mapping feature classes scalable for large-scale optimization. Our
results also show that features computed by the proposed framework are
beneficial for predicting the high-level properties of the 24 large-scale BBOB
functions.
