On Crossover Success Rate in Genetic Programming with Offspring Selection

  • Gabriel Kronberger
  • Stephan Winkler
  • Michael Affenzeller
  • Stefan Wagner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5481)


A lot of progress towards a theoretic description of genetic programming in form of schema theorems has been made, but the internal dynamics and success factors of genetic programming are still not fully understood. In particular, the effects of different crossover operators in combination with offspring selection are still largely unknown. This contribution sheds light on the ability of well-known GP crossover operators to create better offspring (success rate) when applied to benchmark problems. We conclude that standard (sub-tree swapping) crossover is a good default choice in combination with offspring selection, and that GP with offspring selection and random selection of crossover operators does not improve the performance of the algorithm in terms of best solution quality or efficiency.


Mean Square Error Selection Pressure Genetic Programming Solution Quality Tree Size 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Gabriel Kronberger
    • 1
  • Stephan Winkler
    • 1
  • Michael Affenzeller
    • 1
  • Stefan Wagner
    • 1
  1. 1.Heuristic and Evolutionary Algorithms Laboratory School of InformaticsCommunications and Media - Hagenberg, Upper Austria University of Applied SciencesHagenbergAustria

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