Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
The Code Book: The Evolution of Secrecy from Mary, Queen of Scots, to Quantum Cryptography
The Code Book: The Evolution of Secrecy from Mary, Queen of Scots, to Quantum Cryptography
Journal of Global Optimization
Cryptanalysis of four-rounded DES using binary particle swarm optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Designing stream cipher systems using genetic programming
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
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Modern ciphers constitute a challenge task for cryptanalysis algorithms due to their diversity and nonlinearity. This problem can be formulated as NP-Hard. It has long been subject to various attacks; available results, emerged many years ago remain insufficient when handling large instances due to resources requirement which increase with the size of the problem. On another side, computational intelligence represents a set of methodologies used to solve difficult optimization problems. This is mainly due to their ability to converge with acceptable resource consumption. The purpose of this paper is to provide a more detailed study of the performance of three computational metaheuristics: Particle swarm optimization, differential evolution and for a first time, genetic programing for cryptanalysis of a simplified variant of Data encryption standard algorithm. Experiments were performed to study the effectiveness of these algorithms in solving the considered problem and underline the difficulties encountered.