  For many real-world optimization problems it is possible to perform partial
evaluations, meaning that the impact of changing a few variables on a
solution's fitness can be computed very efficiently. It has been shown that
such partial evaluations can be excellently leveraged by the Real-Valued GOMEA
(RV-GOMEA) that uses a linkage model to capture dependencies between problem
variables. Recently, conditional linkage models were introduced for RV-GOMEA,
expanding its state-of-the-art performance even to problems with overlapping
dependencies. However, that work assumed that the dependency structure is known
a priori. Fitness-based linkage learning techniques have previously been used
to detect dependencies during optimization, but only for non-conditional
linkage models. In this work, we combine fitness-based linkage learning and
conditional linkage modelling in RV-GOMEA. In addition, we propose a new way to
model overlapping dependencies in conditional linkage models to maximize the
joint sampling of fully interdependent groups of variables. We compare the
resulting novel variant of RV-GOMEA to other variants of RV-GOMEA and VkD-CMA
on 12 problems with varying degree of overlapping dependencies. We find that
the new RV-GOMEA not only performs best on most problems, also the overhead of
learning the conditional linkage models during optimization is often
negligible.
