- 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 = "
http://doi.acm.org/10.1145/2739480.2754778",
-
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
parent.
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)",
