Degree is Important: On Evolving Homogeneous Boolean Functions

Boolean functions with good cryptographic properties like high nonlinearity and algebraic degree play an important role in the security of stream and block ciphers. Such functions may be designed, for instance, by algebraic constructions or metaheuristics. This paper investigates the use of Evolutionary Algorithms (EAs) to design homogeneous bent Boolean functions, i.e., functions that are maximally nonlinear and whose algebraic normal form contains only monomials of the same degree. In our work, we evaluate three genotype encodings and four fitness functions. Our results show that, while EAs manage to find quadratic homogeneous bent functions (with the best method being a GA leveraging a restricted encoding), none of the approaches result in cubic homogeneous bent functions.

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Publication Details

Journal
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Published
2025-07-14
DOI
https://doi.org/10.1145/3712255.3726779
Citations
2
Primary Topic
semigroups and automata theory
Type
article
Field-Weighted Citation Impact
4.36
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article

Degree is Important: On Evolving Homogeneous Boolean Functions

Stjepan Picek, Marko Ðurasević, Domagoj Jakobović, Luca Mariot et al.
2 citations
Proceedings of the Genetic and Evolutionary Computation Conference Companion
semigroups and automata theory
4.36
article

Degree is Important: On Evolving Homogeneous Boolean Functions

Stjepan Picek, Marko Ðurasević, Domagoj Jakobović, Luca Mariot, Claude Carlet
article en
2 citations

Abstract

Boolean functions with good cryptographic properties like high nonlinearity and algebraic degree play an important role in the security of stream and block ciphers. Such functions may be designed, for instance, by algebraic constructions or metaheuristics. This paper investigates the use of Evolutionary Algorithms (EAs) to design homogeneous bent Boolean functions, i.e., functions that are maximally nonlinear and whose algebraic normal form contains only monomials of the same degree. In our work, we evaluate three genotype encodings and four fitness functions. Our results show that, while EAs manage to find quadratic homogeneous bent functions (with the best method being a GA leveraging a restricted encoding), none of the approaches result in cubic homogeneous bent functions.

Proceedings of the Genetic and Evolutionary Computation Conference Companion
Radboud University Nijmegen (NL), University of Zagreb (HR), University of Bergen (NO), University of Twente (NL)
Openalex Percentile: Top 4%
semigroups and automata theory
4.36
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