Darwinian Data Structure Selection

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Misc{DBLP:journals/corr/BasiosLWKLB17,

• 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

Citations