  We explore the use of Intrinsic Dimension (ID) for gaining insights in how
populations evolve in Evolutionary Algorithms. ID measures the minimum number
of dimensions needed to accurately describe a dataset and its estimators are
being used more and more in Machine Learning to cope with large datasets. We
postulate that ID can provide information about population which is
complimentary w.r.t.\ what (a simple measure of) diversity tells. We
experimented with the application of ID to populations evolved with a recent
variant of Grammatical Evolution. The preliminary results suggest that
diversity and ID constitute two different points of view on the population
dynamics.
