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Using Subtree Crossover Distance to Investigate Genetic Programming Dynamics

  • Leonardo Vanneschi
  • Steven Gustafson
  • Giancarlo Mauri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3905)

Abstract

To analyse various properties of the search process of genetic programming it is useful to quantify the distance between two individuals. Using operator-based distance measures can make this analysis more accurate and reliable than using distance measures which have no relationship with the genetic operators. This paper extends a recent definition of a distance measure based on subtree crossover for genetic programming. Empirical studies are presented that show the suitability of this measure to dynamically calculate the fitness distance correlation coefficient during the evolution, to construct a fitness sharing system for genetic programming and to measure genotypic diversity in the population. These experiments confirm the accuracy of the new measure and its consistency with the subtree crossover genetic operator.

Keywords

Global Optimum Genetic Programming Edit Distance Share System Structural Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Leonardo Vanneschi
    • 1
  • Steven Gustafson
    • 2
  • Giancarlo Mauri
    • 1
  1. 1.Dipartimento di Informatica, Sistemistica e Comunicazione (D.I.S.Co.)University of Milano-BicoccaMilanItaly
  2. 2.School of Computer Science & ITUniversity of NottinghamNottinghamUnited Kingdom

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