Advanced Population Diversity Measures in Genetic Programming

  • Edmund Burke
  • Steven Gustafson
  • Graham Kendall
  • Natalio Krasnogor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)


This paper presents a survey and comparison of significant diversity measures in the genetic programming literature. This study builds on previous work by the authors to gain a deeper understanding of the conditions under which genetic programming evolution is successful. Three benchmark problems (Artificial Ant, Symbolic Regression and Even-5-Parity) are used to illustrate different diversity measures and to analyse their correlation with performance. Results show that measures of population diversity based on edit distances and phenotypic diversity suggest that successful evolution occurs when populations converge to a similar structure but with high fitness diversity.


Genetic Programming Diversity Measure Edit Distance Random Experiment Symbolic Regression 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Edmund Burke
    • 1
  • Steven Gustafson
    • 1
  • Graham Kendall
    • 1
  • Natalio Krasnogor
    • 1
  1. 1.ASAP Research, School of Computer Science & ITUniversity of NottinghamUK

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