Optimising Darwinian Data Structures on Google Guava

  • Michail Basios
  • Lingbo Li
  • Fan Wu
  • Leslie Kanthan
  • Earl T. Barr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10452)


Data structure selection and tuning is laborious but can vastly improve application performance and memory footprint. In this paper, we demonstrate how artemis, a multiobjective, cloud-based optimisation framework can automatically find optimal, tuned data structures and how it is used for optimising the Guava library. From the proposed solutions that artemis found, \(27.45\%\) of them improve all measures (execution time, CPU usage, and memory consumption). More specifically, artemis managed to improve the memory consumption of Guava by up 13%, execution time by up to 9%, and 4% CPU usage.


Search-based software engineering Genetic improvement Software analysis and optimisation Multi-objective optimisation 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Michail Basios
    • 1
  • Lingbo Li
    • 1
  • Fan Wu
    • 1
  • Leslie Kanthan
    • 1
  • Earl T. Barr
    • 1
  1. 1.Department of Computer ScienceUniversity College LondonLondonUK

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