Impact of Crossover Bias in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Nicholas Freitag McPhee and M. Kirbie Dramdahl and 
                 David Donatucci",
  title =        "Impact of Crossover Bias in Genetic Programming",
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "1079--1086",
  keywords =     "genetic algorithms, genetic programming",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "",
  DOI =          "doi:10.1145/2739480.2754778",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In tree-based genetic programming (GP) with sub-tree
                 crossover, the parent contributing the root portion of
                 the tree (the root parent) often contributes more to
                 the semantics of the resulting child than the non-root
                 parent. Previous research demonstrated that when the
                 root parent had greater fitness than the non-root
                 parent, the fitness of the child tended to be better
                 than if the reverse were true. Here we explore the
                 significance of that asymmetry by introducing the
                 notion of crossover bias, where we bias the system in
                 favor of having the more fit parent as the root

                 In this paper we apply crossover bias to several
                 problems. In most cases we found that crossover bias
                 either improved performance or had no impact. We also
                 found that the effectiveness of crossover bias is
                 dependent on the problem, and significantly dependent
                 on other parameter choices.

                 While this work focuses specifically on sub-tree
                 crossover in tree-based GP, artificial and biological
                 evolutionary systems often have substantial
                 asymmetries, many of which remain understudied. This
                 work suggests that there is value in further
                 exploration of the impacts of these asymmetries.",
  notes =        "Also known as \cite{2754778} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",

Genetic Programming entries for Nicholas Freitag McPhee M Kirbie Dramdahl David Donatucci