'On the Application of Hierarchical Coevolutionary Genetic Algorithms: Recombination and Evaluation Partners'
Aickelin, Uwe; Bull, Larry
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.
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|
|Keywords||Genetic Algorithms, Coevolution, Scheduling|
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