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Parallel Problem Solving from Nature — PPSN VII

Volume 2439 of the series Lecture Notes in Computer Science pp 341-350

Date:

Advanced Population Diversity Measures in Genetic Programming

  • Edmund BurkeAffiliated withASAP Research, School of Computer Science & IT, University of Nottingham
  • , Steven GustafsonAffiliated withASAP Research, School of Computer Science & IT, University of Nottingham
  • , Graham KendallAffiliated withASAP Research, School of Computer Science & IT, University of Nottingham
  • , Natalio KrasnogorAffiliated withASAP Research, School of Computer Science & IT, University of Nottingham

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Abstract

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.