scholarly journals Design of a Hybrid Programmable 2-D Cellular Automata Based Pseudo Random Number Generator

2020 ◽  
Vol 8 (6) ◽  
pp. 5741-5748
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Hamming Distance ◽  
Random Number Generator ◽  
Number Generator ◽  
Two Dimensional ◽  
Pseudo Random Number ◽  
Rule Set ◽  
Initial Seed ◽  
Pseudo Random Number Generator

This paper proposes a hybrid programmable two-dimensional Cellular Automata (CA) based pseudo-random number generator which includes a newly designed rule set. The properties and evolution of one and two dimensional CA are revisited. The various metrics for evaluating CA as a Pseudo-Random Number Generator (PRNG) are discussed. It is proved that the randomness is high irrespective of the initial seed by applying this newly designed rule set. The PRNG is tested against a popular statistical test called Diehard test suite and the results show that the PRNG is highly random. The chaotic measures like entropy, hamming distance and cycle length have been measured

scholarly journals Optimization of 1D and 2D Cellular Automata for Pseudo Random Number Generator

2014 ◽  
Vol 4 (6) ◽  
pp. 28-33 ◽  
Author(s):  
P Sudhakar ◽  
◽  
B Chinnarao ◽  
Dr. M. Madhavi Latha
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

Hardware implementation of the elitist compact Genetic Algorithm using Cellular Automata pseudo-random number generator

2013 ◽  
Vol 39 (4) ◽  
pp. 1367-1379 ◽  
Author(s):  
Marco A. Moreno-Armendáriz ◽  
Nareli Cruz-Cortés ◽  
Carlos A. Duchanoy ◽  
Alejandro León-Javier ◽  
Rolando Quintero
Keyword(s):  
Genetic Algorithm ◽  
Cellular Automata ◽  
Hardware Implementation ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Pseudo Random Number ◽  
Compact Genetic Algorithm ◽  
Pseudo Random Number Generator

Design of a Cryptographically Secure Pseudo Random Number Generator with Grammatical Evolution

2021 ◽  
Author(s):  
Conor Ryan ◽  
Meghana Kshirsagar ◽  
Gauri Vaidya ◽  
Andrew Cunningham ◽  
R Sivaraman
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Grammatical Evolution ◽  
Random Numbers ◽  
Number Generator ◽  
Pseudo Random Number ◽  
Initial Seed ◽  
Discernible Difference ◽  
Pseudo Random Number Generator ◽  
Entropy Source

Abstract This work investigates the potential of evolving an initial seed with Grammatical Evolution (GE), for the construction of cryptographically secure (CS) pseudo-random number generator (PRNG). We harness the flexibility of GE as an entropy source for returning initial seeds. The initial seeds returned by GE demonstrate an average entropy value of 7.920261600000001 which is extremely close to the ideal value of 8. The initial seed combined with our proposed approach, control_flow_incrementor, is used to construct both, GE-PRNG and GE-CSPRNG.The random numbers generated with CSPRNG meet the prescribed National Institute of Standards and Technology (NIST) SP800-22 requirements. Monte Carlo simulations established the efficacy of the PRNG. The experimental setup was designed to estimate the value for pi, in which 100,000,000 random numbers were generated by our system and which resulted in returning the value of pi to 3.146564000, with a precision up to six decimal digits. The random numbers by GE-PRNG were compared against those generated by Python’s rand() function for sampling. The sampling results, when measured for accuracy against twenty-nine real world regression datasets, showed that GE-PRNG had less error when compared to Python’s rand() against the ground truths in seventeen of those, while there was no discernible difference in the remaining twelve.

Maximal length cellular automata in GF(q) and pseudo-random number generation

2020 ◽  
Vol 31 (03) ◽  
pp. 2050037
Author(s):  
Sumit Adak ◽  
Kamalika Bhattacharjee ◽  
Sukanta Das
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Maximal Length ◽  
Number Generator ◽  
Greedy Strategy ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

This work explores the randomness quality of maximal length cellular automata (CAs) in GF([Formula: see text]), where [Formula: see text]. A greedy strategy is chosen to select the candidate CAs which satisfy unpredictability criterion essential for a good pseudo-random number generator (PRNG). Then, performance of these CAs as PRNGs is empirically analyzed by using Diehard battery of tests. It is observed that, up to GF(11), increase in [Formula: see text] improves randomness quality of the CAs, but after that, it saturates. Finally, we propose an implementable design of a good PRNG based on a 13-cell maximal length cellular automaton over GF(11) which can compete with the existing well-known PRNGs.

scholarly journals Design of New Pseudo-Random Number Generator Based on Non-Uniform Cellular Automata

2016 ◽  
Vol 10 (11) ◽  
pp. 109-118 ◽  
Author(s):  
Charifa Hanin ◽  
Fouzia Omary ◽  
Souad Elbernoussi ◽  
Bouchra Boulahiat
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

The Pseudorandom Number Generators Based on Cellular Automata With Inhomogeneous Cells

2017 ◽  
pp. 109-126
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Local Function ◽  
Random Number Generators ◽  
Hybrid Cellular Automata ◽  
Main Cell ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

The fifth chapter deals with the use of hybrid cellular automata for constructing high-quality pseudo-random number generators. A hybrid cellular automaton consists of homogeneous cells and a small number of inhomogeneous cells. Inhomogeneous cells perform a local function that differs from local functions that homogeneous cells realize. The location of inhomogeneous cells and the main cell is chosen in advance. The output of the main cell is the output of a pseudo-random number generator. A hardware implementation of a pseudo-random number generator based on hybrid cellular automata is described. The local function that an inhomogeneous cell realizes is the majority function. The principles of constructing a pseudo-random number generator based on cellular automata with inhomogeneous neighborhoods are described. In such cellular automata, inhomogeneous cells have a neighborhood whose shape differs from that of neighborhoods of homogeneous cells.

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