Evolutionary Design of Fast High-quality Hash Functions for Network Applications

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Grochol:2016:GECCO,
  author =       "David Grochol and Lukas Sekanina",
  title =        "Evolutionary Design of Fast High-quality Hash
                 Functions for Network Applications",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich and Frank Neumann and 
                 Andrew M. Sutton and Martin Middendorf and Xiaodong Li and 
                 Emma Hart and Mengjie Zhang and Youhei Akimoto and 
                 Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and 
                 Daniele Loiacono and Julian Togelius and 
                 Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and 
                 Faustino Gomez and Carlos M. Fonseca and 
                 Heike Trautmann and Alberto Moraglio and William F. Punch and 
                 Krzysztof Krawiec and Zdenek Vasicek and 
                 Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and 
                 Boris Naujoks and Enrique Alba and Gabriela Ochoa and 
                 Simon Poulding and Dirk Sudholt and Timo Koetzing",
  pages =        "901--908",
  keywords =     "genetic algorithms, genetic programming",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908825",
  abstract =     "High speed networks operating at 100 Gbps pose many
                 challenges for hardware and software involved in the
                 packet processing. As the time to process one packet is
                 very short the corresponding operations have to be
                 optimized in terms of the execution time. One of them
                 is non-cryptographic hashing implemented in order to
                 accelerate traffic flow identification. In this paper,
                 a method based on linear genetic programming is
                 presented, which is capable of evolving high-quality
                 hash functions primarily optimized for speed. Evolved
                 hash functions are compared with conventional hash
                 functions in terms of accuracy and execution time using
                 real network data.",
  notes =        "GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",
}

Genetic Programming entries for David Grochol Lukas Sekanina

Citations