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This paper combines the idea of a hierarchical distributed genetic algorithm with different interagent 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 subpopulations search a larger search space with a lower resolution whilst lower-level subpopulations 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 multiplechoice optimisation problems. It is shown that partnering strategies that exploit problemspecific knowledge are superior and can counter inappropriate (sub-) fitness measurements.

A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems