Multi-objective evolution of hash functions for high speed networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{grochol:2017:CEC,
-
author = "David Grochol and Lukas Sekanina",
-
booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)",
-
title = "Multi-objective evolution of hash functions for high
speed networks",
-
year = "2017",
-
editor = "Jose A. Lozano",
-
pages = "1533--1540",
-
address = "Donostia, San Sebastian, Spain",
-
publisher = "IEEE",
-
isbn13 = "978-1-5090-4601-0",
-
abstract = "Hashing is a critical function in capturing and
analysis of network flows as its quality and execution
time influences the maximum throughput of network
monitoring devices. In this paper, we propose a
multi-objective linear genetic programming approach to
evolve fast and high-quality hash functions for common
processors. The search algorithm simultaneously
optimizes the quality of hashing and the execution
time. As it is very time consuming to obtain the real
execution time for a candidate solution on a particular
processor, the execution time is estimated in the
fitness function. In order to demonstrate the
superiority of the proposed approach, evolved hash
functions are compared with hash functions available in
the literature using real-world network data.",
-
keywords = "genetic algorithms, genetic programming, cryptography,
critical function, fitness function, hash functions,
hashing, high speed networks, multiobjective evolution,
multiobjective linear genetic programming, network
flows, network monitoring devices, real-world network
data, search algorithm, Hardware, Monitoring, Program
processors, Registers",
-
isbn13 = "978-1-5090-4601-0",
-
DOI = "doi:10.1109/CEC.2017.7969485",
-
month = "5-8 " # jun,
-
notes = "IEEE Catalog Number: CFP17ICE-ART Also known as
\cite{7969485}",
- }
Genetic Programming entries for
David Grochol
Lukas Sekanina
Citations