  This paper examines the use of a hierarchical coevolutionary genetic
algorithm under different partnering strategies. Cascading clusters of
sub-populations are built from the bottom up, with higher-level sub-populations
optimising larger parts of the problem. Hence higher-level sub-populations
potentially search a larger search space with a lower resolution whilst
lower-level sub-populations search a smaller search space with a higher
resolution. The effects of different partner selection schemes amongst the
sub-populations on solution quality are examined for two constrained
optimisation problems. We examine a number of recombination partnering
strategies in the construction of higher-level individuals and a number of
related schemes for evaluating sub-solutions. It is shown that partnering
strategies that exploit problem-specific knowledge are superior and can counter
inappropriate (sub)fitness measurements.
