Publication Details
Multiobjective Evolution of Hash Functions for High Speed Networks
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
NSGA-II, linear genetic programming, hash function, network
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.
@inproceedings{BUT144407,
author="David {Grochol} and Lukáš {Sekanina}",
title="Multiobjective Evolution of Hash Functions for High Speed Networks",
booktitle="Proceedings of the 2017 IEEE Congress on Evolutionary Computation",
year="2017",
pages="1533--1540",
publisher="IEEE Computer Society",
address="San Sebastian",
doi="10.1109/CEC.2017.7969485",
isbn="978-1-5090-4600-3",
url="https://www.fit.vut.cz/research/publication/11325/"
}