Designing Bent Boolean Functions with Parallelized Linear Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Husa:2017:GECCO,

• author = "Jakub Husa and Roland Dobai",
• title = "Designing Bent {Boolean} Functions with Parallelized Linear Genetic Programming",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion",
• series = "GECCO '17",
• year = "2017",
• isbn13 = "978-1-4503-4939-0",
• address = "Berlin, Germany",
• pages = "1825--1832",
• size = "8 pages",
• URL = "https://www.fit.vut.cz/research/publication/11402/.cs",
• URL = "http://doi.acm.org/10.1145/3067695.3084220",
• DOI = "doi:10.1145/3067695.3084220",
• acmid = "3084220",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, bent functions, boolean functions, cryptography, island model, linear genetic programming, nonlinearity",
• month = "15-19 " # jul,
• abstract = "Bent Boolean functions are cryptographic primitives essential for the safety of cryptographic algorithms, providing a degree of non-linearity to otherwise linear systems. The maximum possible non-linearity of a Boolean function is limited by the number of its inputs, and as technology advances, functions with higher number of inputs are required in order to guarantee a level of security demanded in many modern applications. Genetic programming has been successfully used to discover new larger bent Boolean functions in the past. This paper proposes the use of linear genetic programming for this purpose. It shows that this approach is suitable for designing of bent Boolean functions larger than those designed using other approaches, and explores the influence of multiple evolutionary parameters on the evolution runtime. Parallelized implementation of the proposed approach is used to search for new, larger bent functions, and the results are compared with other related work. The results show that linear genetic programming copes better with growing number of function inputs than genetic programming, and is able to create significantly larger bent functions in comparable time.",
• notes = "Also known as \cite{Husa:2017:DBB:3067695.3084220} \cite{FITPUB11402} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)",

}

Genetic Programming entries for Jakub Husa Roland Dobai

Citations