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arXiv · 0801.3550

Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm

Uwe Aickelin·Larry Bull
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Abstract

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 for (sub-)fitness evaluation purposes are examined for two multiple-choice optimisation problems. It is shown that random partnering strategies perform best by providing better sampling and more diversity.

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Uwe Aickelin, Larry Bull. 2008-03-03. Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm. https://arxiv.org/abs/0801.3550

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