Skip to main content
Article thumbnail
Location of Repository

Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm

By Uwe Aickelin and Larry Bull


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:
Provided by: Nottingham ePrints

Suggested articles


  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.”

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.