On crossover success rate in genetic programming with offspring selection

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Abstract

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.

Original languageEnglish
Title of host publicationGenetic Programming - 12th European Conference, EuroGP 2009, Proceedings
Pages232-243
Number of pages12
Volume5481
Edition1
DOIs
Publication statusPublished - 2009
Event12th European Conference on Genetic Programming, EuroGP 2009 - Tubingen, Germany
Duration: 15 Apr 200917 Apr 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5481 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th European Conference on Genetic Programming, EuroGP 2009
Country/TerritoryGermany
CityTubingen
Period15.04.200917.04.2009

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