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

Aickelin, Uwe; Bull, Larry

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


Authors

Uwe Aickelin

Larry Bull



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

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