'On the Application of Hierarchical Coevolutionary Genetic Algorithms: Recombination and Evaluation Partners'

Aickelin, Uwe; Bull, Larry

Abstract

This paper examines the use of a hierarchical coevolutionary genetic algorithm under different partnering strategies. Cascading clusters of sub-populations are built from the bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations potentially 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 amongst the sub-populations on solution quality are examined for two constrained optimisation problems. We examine a number of recombination partnering strategies in the construction of higher-level individuals and a number of related schemes for evaluating sub-solutions. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements.

Citation

Aickelin, U., & Bull, L. (2003). 'On the Application of Hierarchical Coevolutionary Genetic Algorithms: Recombination and Evaluation Partners'

• Journal Article Type: Article
• Publication Date: Jan 1, 2003
• Deposit Date: Nov 7, 2005
• Publicly Available Date: Dec 4, 2007
• Journal: Journal of Applied System Studies
• Peer Reviewed: Peer Reviewed
• Volume: 4
• Issue: 2
• Keywords: Genetic Algorithms, Coevolution, Scheduling
• Public URL: https://nottingham-repository.worktribe.com/output/1022126