Semantic mutation operator for a fast and efficient design of bent Boolean functions
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- @Article{Husa:2024:GPEM,
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author = "Jakub Husa and Lukas Sekanina",
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title = "Semantic mutation operator for a fast and efficient
design of bent Boolean functions",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2024",
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volume = "25",
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pages = "Article number: 3",
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note = "Online first",
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keywords = "genetic algorithms, genetic programming, Nonlinearity,
Bent Boolean functions, Heuristic optimization,
Semantic mutation",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-023-09476-w",
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abstract = "Boolean functions are important cryptographic
primitives with extensive use in symmetric
cryptography. These functions need to possess various
properties, such as nonlinearity to be useful. The main
limiting factor of the quality of a Boolean function is
the number of its input variables, which has to be
sufficiently large. The contemporary design methods
either scale poorly or are able to create only a small
subset of all functions with the desired properties.
This necessitates the development of new and more
efficient ways of Boolean function design. we propose a
new semantic mutation operator for the design of bent
Boolean functions via genetic programming. The
principle of the proposed operator lies in evaluating
the function's nonlinearity in detail to purposely
avoid mutations that could be disruptive and taking
advantage of the fact that the nonlinearity of a
Boolean function is invariant under all affine
transformations. To assess the efficiency of this
operator, we experiment with three distinct variants of
genetic programming and compare its performance to
three other commonly used non-semantic mutation
operators. The detailed experimental evaluation proved
that the proposed semantic mutation operator is not
only significantly more efficient in terms of
evaluations required by genetic programming but also
nearly three times faster than the second-best operator
when designing bent functions with 12 inputs and almost
six times faster for functions with 20 inputs.",
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notes = "Faculty of Information Technology, Brno University of
Technology, Božetěchova 2/1, Brno, 612 00, Czech
Republic",
- }
Genetic Programming entries for
Jakub Husa
Lukas Sekanina
Citations