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 subpopulations are built from the bottom up, with higher-level subpopulations optimising larger parts of the problem. Hence higher-level subpopulations potentially search a larger search space with a lower resolution whilst lower-level subpopulations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the subpopulations 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.