Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm

Details Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm

2008-01-23

arxiv.org/abs/0801.3550v2

Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), pp 263-270, New York, USA, 2002

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 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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