Heuristic Search by Particle Swarm Optimization of Boolean Functions for Cryptographic Applications

Abstract : We present a Particle Swarm Optimizer for generating boolean functions with good cryptographic properties. The proposed algorithm updates the particles positions while preserving their Hamming weights, to ensure that the generated functions are balanced, and it adopts Hill Climbing to further improve their nonlinearity and correlation immunity. The results of the optimization experiments for n=7 to n=12 variables show that this new PSO algorithm finds boolean functions with good trade-offs of nonlinearity, resiliency and Strict Avalanche Criterion.
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Communication dans un congrès
GECCO Companion '15 Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, Jul 2015, Madrid, Spain. pp.1425-1426, 2015, 〈10.1145/2739482.2764674〉
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https://hal.archives-ouvertes.fr/hal-01313904
Contributeur : Luca Mariot <>
Soumis le : mardi 10 mai 2016 - 15:25:03
Dernière modification le : mercredi 11 mai 2016 - 01:07:28

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Luca Mariot, Alberto Leporati. Heuristic Search by Particle Swarm Optimization of Boolean Functions for Cryptographic Applications. GECCO Companion '15 Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, Jul 2015, Madrid, Spain. pp.1425-1426, 2015, 〈10.1145/2739482.2764674〉. 〈hal-01313904〉

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