Observing the Population Dynamics in GE by means of the Intrinsic Dimension
This work addresses the challenge of analyzing population evolution in evolutionary computation, but it appears incremental as it builds on existing ID methods without introducing major innovations.
The paper tackled the problem of understanding population dynamics in evolutionary algorithms by applying Intrinsic Dimension (ID) as a complementary measure to diversity, with preliminary results indicating that ID and diversity offer distinct perspectives on population evolution.
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.