Darwinian Data Structure Selection

Created by W.Langdon from gp-bibliography.bib Revision:1.4472

  author =       "Michail Basios and Lingbo Li and Fan Wu and 
                 Leslie Kanthan and Donald Lawrence and Earl T. Barr",
  title =        "Darwinian Data Structure Selection",
  howpublished = "arXiv",
  year =         "2017",
  month =        "10 " # jun,
  keywords =     "genetic algorithms, genetic programming, genetic
                 improvement, Search-based software engineering, SBSE,
                 Software analysis and optimisation, Multi-objective
                 optimisation, SBSE, Software Engineering",
  timestamp =    "Tue, 29 Aug 2017 15:03:42 +0200",
  biburl =       "http://dblp.uni-trier.de/rec/bib/journals/corr/BasiosLWKLB17",
  bibsource =    "dblp computer science bibliography, http://dblp.org",
  URL =          "http://arxiv.org/abs/1706.03232",
  size =         "11 pages",
  abstract =     "Data structure selection and tuning is laborious but
                 can vastly improve application performance and memory
                 footprint. We introduce ARTEMIS a multiobjective,
                 cloud-based optimisation framework that automatically
                 finds optimal, tuned data structures and rewrites
                 applications to use them. ARTEMIS achieves substantial
                 performance improvements for every project in a set of
                 29 Java programs uniformly sampled from GitHub. For
                 execution time, CPU usage, and memory consumption,
                 ARTEMIS finds at least one solution for each project
                 that improves all measures. The median improvement
                 across all these best solutions is 8.38percent for
                 execution time, 24.27percent for memory consumption and
                 11.61percent for CPU usage. In detail, ARTEMIS improved
                 the memory consumption of JUnit4, a ubiquitous Java
                 testing framework, by 45.42percent memory, while also
                 improving its execution time 2.29percent at the cost a
                 1.25percent increase in CPU usage. LinkedIn relies on
                 the Cleo project as their autocompletion engine for
                 search. ARTEMIS improves its execution time by
                 12.17percent, its CPU usage by 4.32percent and its
                 memory consumption by 23.91percent.",

Genetic Programming entries for Michail Basios Lingbo Li Fan Wu Leslie Kanthan Donald Lawrence Earl Barr