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

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 problemspecific knowledge are superior and can counter inappropriate (sub-) fitness measurements.

• Type: Journal
• Year: 2008
• Where: CORR
• Authors: Uwe Aickelin, Larry Bull
• Keywords: Coevolutionary Genetic Algorithm, CORR 2008, Education, Larger Search Space, Search Space