Evolutionary computation in computer security and cryptography
New Generation Computing - Evolutionary computation
Randomness quality of permuted pseudorandom binary sequences
Mathematics and Computers in Simulation
An Approach to Searching for Two-Dimensional Cellular Automata for Recognition of Handwritten Digits
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
A high-quality pseudorandom numbers generator based on twi-layer couple cellular automata
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Cryptanalysis of an image encryption scheme using cellular automata substitution and SCAN
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Synchronization via multiplex spike-trains in digital pulse coupled networks
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Conflict detection in role-based access control using multiple-attractor cellular automata
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
On the combination of self-organized systems to generate pseudo-random numbers
Information Sciences: an International Journal
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Cellular automata (CA) has been used in pseudorandom number generation for over a decade. Recent studies show that two-dimensional (2-D) CA pseudorandom number generators (PRNGs) may generate better random sequences than conventional one-dimensional (1-D) CA PRNGs, but they are more complex to implement in hardware than 1-D CA PRNGs. In this paper, we propose a new class of 1-D CA - controllable cellular automata (CCA)-without much deviation from the structural simplicity of conventional 1-D CA. We first give a general definition of CCA and then introduce two types of CCA: CCA0 and CCA2. Our initial study shows that these two CCA PRNGs have better randomness quality than conventional 1-D CA PRNGs, but that their randomness is affected by their structures. To find good CCA0/CCA2 structures for pseudorandom number generation, we evolve them using evolutionary multiobjective optimization techniques. Three different algorithms are presented. One makes use of an aggregation function; the other two are based on the vector-evaluated genetic algorithm. Evolution results show that these three algorithms all perform well. Applying a set of randomness tests on the evolved CCA PRNGs, we demonstrate that their randomness is better than that of 1-D CA PRNGs and can be comparable to that of 2-D CA PRNGs.