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Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm

By Uwe Aickelin and Larry Bull

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

Year: 2002
OAI identifier: oai:eprints.nottingham.ac.uk:633
Provided by: Nottingham ePrints

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Citations

  1. (1994). Altruism in the Evolution of Communication”,
  2. (1998). Coevolving Functions in Genetic Programming: Dynamic ADF Creation using GliB”.
  3. (1999). Genetic Algorithms for MultipleChoice Optimisation Problems.”

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