Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm

Aickelin, Uwe; Bull, Larry

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.

Citation

Aickelin, U., & Bull, L. (2002). Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm.

• Conference Name: Genetic and Evolutionary Computation Conference
• Publication Date: Jan 1, 2002
• Deposit Date: Oct 12, 2007
• Publicly Available Date: Oct 12, 2007
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/1022640