  Hypervolume contribution is an important concept in evolutionary
multi-objective optimization (EMO). It involves in hypervolume-based EMO
algorithms and hypervolume subset selection algorithms. Its main drawback is
that it is computationally expensive in high-dimensional spaces, which limits
its applicability to many-objective optimization. Recently, an R2 indicator
variant (i.e., $R_2^{\text{HVC}}$ indicator) is proposed to approximate the
hypervolume contribution. The $R_2^{\text{HVC}}$ indicator uses line segments
along a number of direction vectors for hypervolume contribution approximation.
It has been shown that different direction vector sets lead to different
approximation quality. In this paper, we propose \textit{Learning to
Approximate (LtA)}, a direction vector set generation method for the
$R_2^{\text{HVC}}$ indicator. The direction vector set is automatically learned
from training data. The learned direction vector set can then be used in the
$R_2^{\text{HVC}}$ indicator to improve its approximation quality. The
usefulness of the proposed LtA method is examined by comparing it with other
commonly-used direction vector set generation methods for the
$R_2^{\text{HVC}}$ indicator. Experimental results suggest the superiority of
LtA over the other methods for generating high quality direction vector sets.
