  When it comes to solving optimization problems with evolutionary algorithms
(EAs) in a reliable and scalable manner, detecting and exploiting linkage
information, i.e., dependencies between variables, can be key. In this article,
we present the latest version of, and propose substantial enhancements to, the
Gene-pool Optimal Mixing Evoutionary Algorithm (GOMEA): an EA explicitly
designed to estimate and exploit linkage information. We begin by performing a
large-scale search over several GOMEA design choices, to understand what
matters most and obtain a generally best-performing version of the algorithm.
Next, we introduce a novel version of GOMEA, called CGOMEA, where linkage-based
variation is further improved by filtering solution mating based on conditional
dependencies. We compare our latest version of GOMEA, the newly introduced
CGOMEA, and another contending linkage-aware EA DSMGA-II in an extensive
experimental evaluation, involving a benchmark set of 9 black-box problems that
can only be solved efficiently if their inherent dependency structure is
unveiled and exploited. Finally, in an attempt to make EAs more usable and
resilient to parameter choices, we investigate the performance of different
automatic population management schemes for GOMEA and CGOMEA, de facto making
the EAs parameterless. Our results show that GOMEA and CGOMEA significantly
outperform the original GOMEA and DSMGA-II on most problems, setting a new
state of the art for the field.
