Genetic Programming and Evolvable Machines

, Volume 5, Issue 3, pp 271–290 | Cite as

Problem Difficulty and Code Growth in Genetic Programming

  • Steven Gustafson
  • Anikó Ekárt
  • Edmund Burke
  • Graham Kendall

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.

genetic programming population diversity code growth problem difficulty 

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Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Steven Gustafson
    • 1
  • Anikó Ekárt
    • 2
  • Edmund Burke
    • 3
  • Graham Kendall
    • 3
  1. 1.School of Computer Science & ITUniversity of NottinghamUK
  2. 2.Computer and Automation Research InstituteHungarian Academy of SciencesBudapestHungary
  3. 3.School of Computer Science & ITUniversity of NottinghamUK

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