Designing Bent Boolean Functions with Parallelized Linear Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4009

@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 =          "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}
                 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