On the Effectiveness of Genetic Operations in Symbolic Regression

TL;DR

This paper presents a novel methodology for analyzing genetic programming in symbolic regression, focusing on genealogical tracing and diversity measures to understand evolutionary dynamics and the origins of optimal solutions.

Contribution

It introduces a new subtree tracing approach and provides insights into the key ancestors responsible for the best solutions in GP populations.

Findings

Few ancestors significantly influence the best solutions

Genealogical analysis reveals the importance of specific individuals

Diversity measures help understand evolutionary progress

Abstract

This paper describes a methodology for analyzing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the fitness variation from parent to offspring. We introduce a new subtree tracing approach for identifying the origins of genes in the structure of individuals, and we show that only a small fraction of ancestor individuals are responsible for the evolvement of the best solutions in the population.