Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm

Tools

Aickelin, Uwe and Bull, Larry (2002) Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm. In: Genetic and Evolutionary Computation Conference, 2002, New York, USA.

This is the latest version of this item.

Full text not available from this repository.

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.

Item Type: Conference or Workshop Item (Paper)
RIS ID: https://nottingham-repository.worktribe.com/output/1022640
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: Aickelin, Professor Uwe
Date Deposited: 12 Oct 2007 15:44
Last Modified: 04 May 2020 20:32
URI: https://eprints.nottingham.ac.uk/id/eprint/633

Available Versions of this Item

Actions (Archive Staff Only)

Edit View Edit View