RT Journal Article
T1 Multilayer analysis of population diversity in grammatical evolution for symbolic regression
A1 Kronberger, Gabriel
A1 Colmenar, Manuel
A1 Winkler, Stephan
A1 Hidalgo Pérez, José Ignacio
AB In this paper, we analyze the population diversity of grammatical evolution (GE) on multiple levels of genetic information: chromosome diversity, expression diversity, and output diversity. Thereby, we use a tree-similarity metric from tree-based GP literature to determine similarity of expression trees generated in GE. The similarity of outputs is determined via their correlation.We track the pairwise similarities for all individualswithin a generation on all three levels and track the distribution of similarity values over generations.We demonstrate the analysis method using four symbolic regression problem instances and find that the visualization highlights some issues that can occur when using GE such as: large groups of individuals with highly similar outputs, a high fraction of trees with constant outputs, or short and highly similar trees in the early stages of the GE run. Especially in the early phases of GE, we see that a large subset of the population represents equivalent expressions. In early stages, rather short expressions are produced leaving large parts of the chromosome unexpressed. More complex expressions can be derived only after GE has successfully evolved well working beginnings of chromosomes
PB Springer Nature
SN 1432-7643
SN 1433-7479
YR 2020
FD 2020
LK https://hdl.handle.net/20.500.14352/117301
UL https://hdl.handle.net/20.500.14352/117301
LA eng
DS Docta Complutense
RD 30 dic 2025