Publication Details

Multi-Objective Evolution of Ultra-Fast General-Purpose Hash Functions

GROCHOL, D.; SEKANINA, L. Multi-Objective Evolution of Ultra-Fast General-Purpose Hash Functions. In European Conference on Genetic Programming. Lecture Notes in Computer Science. Berlin: Springer International Publishing, 2018. p. 187-202. ISBN: 978-3-319-77553-1.
Czech title
Více-kriteriální návrh rychlých univerzálních hašovacích funkcí
Type
conference paper
Language
English
Authors
Grochol David, Ing., Ph.D.
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Keywords

NSGA-II, linear genetic programming, hash function

Abstract

Hashing is an important function in many applications such as hash tables, caches and Bloom filters. In past, genetic programming was applied to evolve application-specific as well as general-purpose hash functions, where the main design target was the quality of hashing. As hash functions are frequently called in various time-critical applications, it is important to optimize their implementation with respect to the execution time. In this paper, linear genetic programming is combined with NSGA-II algorithm in order to obtain general-purpose, ultra-fast and high-quality hash functions. Evolved hash functions show highly competitive quality of hashing, but significantly reduced execution time in comparison with the state of the art hash functions available in literature. 

Published
2018
Pages
187–202
Proceedings
European Conference on Genetic Programming
Series
Lecture Notes in Computer Science
Volume
10781
ISBN
978-3-319-77553-1
Publisher
Springer International Publishing
Place
Berlin
DOI
UT WoS
000787651200012
EID Scopus
BibTeX
@inproceedings{BUT147190,
  author="David {Grochol} and Lukáš {Sekanina}",
  title="Multi-Objective Evolution of Ultra-Fast General-Purpose Hash Functions",
  booktitle="European Conference on Genetic Programming",
  year="2018",
  series="Lecture Notes in Computer Science",
  volume="10781",
  pages="187--202",
  publisher="Springer International Publishing",
  address="Berlin",
  doi="10.1007/978-3-319-77553-1\{_}12",
  isbn="978-3-319-77553-1",
  url="https://www.fit.vut.cz/research/publication/11552/"
}
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