Observing the Population Dynamics in GE by means of the Intrinsic Dimension

TL;DR

This paper investigates the use of Intrinsic Dimension (ID) to analyze population dynamics in Evolutionary Algorithms, proposing that ID offers unique insights beyond traditional diversity measures.

Contribution

It introduces the application of ID to evolutionary populations and demonstrates that ID provides a different perspective on population evolution compared to diversity.

Findings

ID can reveal different aspects of population structure

Preliminary results show ID and diversity are complementary measures

ID helps understand population evolution in Grammatical Evolution

Abstract

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.