  This paper combines the idea of a hierarchical distributed genetic algorithm
with different inter-agent partnering strategies. Cascading clusters of
sub-populations are built from bottom up, with higher-level sub-populations
optimising larger parts of the problem. Hence higher-level sub-populations
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 agents on solution
quality are examined for two multiple-choice optimisation problems. It is shown
that partnering strategies that exploit problem-specific knowledge are superior
and can counter inappropriate (sub-) fitness measurements.
