Exploiting tournament selection for efficient parallel genetic programming

Darren M. Chitty*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)


Genetic Programming (GP) is a computationally intensive technique which is naturally parallel in nature. Consequently, many attempts have been made to improve its run-time from exploiting highly parallel hardware such as GPUs. However, a second methodology of improving the speed of GP is through efficiency techniques such as subtree caching. However achieving parallel performance and efficiency is a difficult task. This paper will demonstrate an efficiency saving for GP compatible with the harnessing of parallel CPU hardware by exploiting tournament selection. Significant efficiency savings are demonstrated whilst retaining the capability of a high performance parallel implementation of GP. Indeed, a 74% improvement in the speed of GP is achieved with a peak rate of 96 billion GPop/s for classification type problems.

Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems
Subtitle of host publicationContributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK
EditorsAhmad Lotfi, Caroline Langensiepen, Hamid Bouchachia, Alexander Gegov, Martin McGinnity
PublisherSpringer, Cham
Number of pages13
ISBN (Electronic)9783319979823
ISBN (Print)9783319979816
Publication statusPublished - 11 Aug 2018
Event18th UK Workshop on Computational Intelligence, UKCI 2018 - Nottingham, United Kingdom
Duration: 5 Sept 20187 Sept 2018

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357


Conference18th UK Workshop on Computational Intelligence, UKCI 2018
Country/TerritoryUnited Kingdom


  • Computational efficiency
  • Genetic programming
  • HPC


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