Problem Difficulty and Code Growth in Genetic Programming

Authors:
Steven Gustafson;Anikó Ekárt;Edmund Burke;Graham Kendall
Affiliations:
School of Computer Science & IT, University of Nottingham, UK NG81BB;Computer and Automation Research Institute, Hungarian Academy of Sciences, Budapest, Hungary 1518;School of Computer Science & IT, University of Nottingham, UK NG81BB;School of Computer Science & IT, University of Nottingham, UK NG81BB
Venue:
Genetic Programming and Evolvable Machines
Year:
2004

Citing 24
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Abstract

This paper investigates the relationship between code growth and problem difficulty in genetic programming. The symbolic regression problem domain is used to investigate this relationship using two different types of increased instance difficulty. Results are supported by a simplified model of genetic programming and show that increased difficulty induces higher selection pressure and less genetic diversity, which both contribute toward an increased rate of code growth.