Computer Science – Neural and Evolutionary Computing
Scientific paper
2008-01-21
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), late-breaking papers volume, pp 1-8, San Fran
Computer Science
Neural and Evolutionary Computing
Scientific paper
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.
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