Evolutionary Construction of Perfectly Balanced Boolean Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Mariot:2022:CEC,

• author = "Luca Mariot and Stjepan Picek and Domagoj Jakobovic and Marko Djurasevic and Alberto Leporati",
• booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Evolutionary Construction of Perfectly Balanced Boolean Functions",
• year = "2022",
• editor = "Carlos A. Coello Coello and Sanaz Mostaghim",
• address = "Padua, Italy",
• month = "18-23 " # jul,
• isbn13 = "978-1-6654-6708-7",
• abstract = "Finding Boolean functions suitable for cryptographic primitives is a complex combinatorial optimization problem, since they must satisfy several properties to resist crypt-analytic attacks, and the space is very large, which grows super exponentially with the number of input variables. Recent research has focused on the study of Boolean functions that satisfy properties on restricted sets of inputs due to their importance in the development of the FLIP stream cipher. In this paper, we consider one such property, perfect balancedness, and investigate the use of Genetic Programming (GP) and Genetic Algorithms (GA) to construct Boolean functions that satisfy this property along with a good nonlinearity profile. We formulate the related optimization problem and define two encodings for the candidate solutions, namely the truth table and the weight wise balanced representations. Somewhat surprisingly, the results show that GA with the weightwise balanced representation outperforms GP with the classical truth table phenotype in finding highly nonlinear Weightwise Perfectly Balanced (WPB) functions. This is in stark contrast to previous findings on the evolution of balanced Boolean functions, where GP always performs best.",
• keywords = "genetic algorithms, genetic programming, Ciphers, Boolean functions, Input variables, Resists, Evolutionary computation, Boolean functions, balancedness, nonlinearity",
• DOI = "doi:10.1109/CEC55065.2022.9870427",
• notes = "Also known as \cite{9870427}",

}

Genetic Programming entries for Luca Mariot Stjepan Picek Domagoj Jakobovic Marko Durasevic Alberto Leporati

Citations