Pseudorandom Number Generators Based on Cellular Automata

2017 ◽  
pp. 52-65
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Pseudorandom Number ◽  
Random Numbers ◽  
Random Number Generators ◽  
One Dimensional ◽  
Pseudorandom Number Generators ◽  
Pseudo Random Number ◽  
The Creation

In this chapter, the author considers the main theoretical solutions for the creation of pseudo-random number generators based on one-dimensional cellular automata. A Wolfram generator is described on the basis of rule 30. The main characteristics of the Wolfram generator is presented. The analysis of hybrid pseudo-random number generators based on cellular automata is carried out. Models of such generators and their realization with various forms of neighborhoods are presented. Also in the chapter is presented the analysis of the basic structures and characteristics of pseudo-random number generators using additional sources of pseudo-random numbers. As such additional sources the LFSR is used.

Pseudorandom Number Generators

2017 ◽  
pp. 1-25
Keyword(s):  
Random Number ◽  
Pseudorandom Number ◽  
Random Numbers ◽  
Pseudorandom Sequences ◽  
Random Number Generators ◽  
Random Sequences ◽  
Pseudorandom Number Generators ◽  
Pseudo Random Number

In this chapter, the author considers existing methods and means of forming pseudo-random sequences of numbers and also are described the main characteristics of random and pseudorandom sequences of numbers. The main theoretical aspects of the construction of pseudo-random number generators are considered. Classification of pseudorandom number generators is presented. The structures and models of the most popular pseudo-random number generators are considered, the main characteristics of generators that affect the quality of the formation of pseudorandom bit sequences are described. The models of the basic mathematical generators of pseudo-random numbers are considered, and also the principles of building hardware generators are presented.

GENERATING PARALLEL RANDOM NUMBER GENERATORS BY CELLULAR PROGRAMMING

1996 ◽  
Vol 07 (02) ◽  
pp. 181-190 ◽  
Author(s):  
MOSHE SIPPER ◽  
MARCO TOMASSINI
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Programming Algorithm ◽  
Random Numbers ◽  
Random Number Generators ◽  
Parallel Random Number Generators

Random numbers are needed in a variety of applications, yet finding good random number generators is a difficult task. In this paper non-uniform cellular automata (CA) are studied, presenting the cellular programming algorithm for co-evolving such CAs to perform computations. The algorithm is applied to the evolution of random number generators; our results suggest that evolved generators are at least as good as previously described CAs, with notable advantages arising from the existence of a "tunable" algorithm for obtaining random number generators.

A list of tri-state cellular automata which are potential pseudo-random number generators

2018 ◽  
Vol 29 (09) ◽  
pp. 1850088 ◽  
Author(s):  
Kamalika Bhattacharjee ◽  
Sukanta Das
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Random Number Generators ◽  
Rule Space ◽  
Pseudo Random Number

This paper explores [Formula: see text]-neighborhood [Formula: see text]-state cellular automata (CAs) to find a list of potential pseudo-random number generators (PRNGs). As the rule-space is huge ([Formula: see text]), we have taken two greedy strategies to select the initial set of rules. These CAs are analyzed theoretically and empirically to find the CAs with consistently good randomness properties. Finally, we have listed [Formula: see text] good CAs which qualify as potential PRNGs.

scholarly journals On pseudorandom number generators

ACTA IMEKO ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 128
Author(s):  
Daniel Chicayban Bastos ◽  
Luis Antonio Brasil Kowada ◽  
Raphael C. S. Machado
Keyword(s):  
Programming Languages ◽  
Random Number ◽  
Statistical Tests ◽  
Statistical Test ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generator ◽  
Statistical Sampling ◽  
Simple Task ◽  
Random Number Generators ◽  
Pseudorandom Number Generators

<p class="Abstract">Statistical sampling and simulations produced by algorithms require fast random number generators; however, true random number generators are often too slow for the purpose, so pseudorandom number generators are usually more suitable. But choosing and using a pseudorandom number generator is no simple task; most pseudorandom number generators fail statistical tests. Default pseudorandom number generators offered by programming languages usually do not offer sufficient statistical properties. Testing random number generators so as to choose one for a project is essential to know its limitations and decide whether the choice fits the project’s objectives. However, this study presents a reproducible experiment that demonstrates that, despite all the contributions it made when it was first published, the popular NIST SP 800-22 statistical test suite as implemented in the software package is inadequate for testing generators.</p>

Itamaracá: A Novel Simple Way to Generate Pseudo-random Numbers

2021 ◽  
Author(s):  
Daniel Henrique Pereira
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Numbers ◽  
Microsoft Excel ◽  
Chi Square ◽  
Random Number Generators ◽  
Pseudo Random Number ◽  
Run Test

In this paper was presented Itamaracá, a novel simple way to generate pseudo random numbers. In general vision we can say that Itamaracá tends to pass in some statistical tests like frequency, chi square, autocorrelation, run sequence and run test. As an effect to comparison also was taking into account the results of the function R and Between by Microsoft Excel and true random numbers by Random Org analyzed its distinctive characteristics as well as with the proposal model. In this sense, the goal of this study is contributing to growing the existing Pseudo Random Number Generators (PRNGs) portfolio.

Itamaracá: A Novel Simple Way to Generate Pseudo-random Numbers

2021 ◽  
Author(s):  
Daniel Henrique Pereira
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Numbers ◽  
Microsoft Excel ◽  
Chi Square ◽  
Random Number Generators ◽  
Pseudo Random Number ◽  
Run Test

In this paper was presented Itamaracá, a novel simple way to generate pseudo random numbers. In general vision we can say that Itamaracá tends to pass in some statistical tests like frequency, chi square, autocorrelation, run sequence and run test. As an effect to comparison also was taking into account the results of the function R and Between by Microsoft Excel and true random numbers by Random Org analyzed its distinctive characteristics as well as with the proposal model. In this sense, the goal of this study is contributing to growing the existing Pseudo Random Number Generators (PRNGs) portfolio.

Influence of Neighborhood Forms on the Quality of Pseudorandom Number Generators' Work Based on Cellular Automata

2020 ◽  
pp. 43-78
Author(s):  
Sergii Bilan
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Initial Conditions ◽  
Experimental Studies ◽  
High Quality ◽  
Random Number Generators ◽  
Optimal Dimension ◽  
Pseudo Random Number ◽  
Nist Tests

The chapter analyzes modern methods for constructing pseudo-random number generators based on cellular automata. Also analyzes the influence of neighborhood forms on the evolution of the functioning of cellular automata, as well as on the quality of the formation of pseudo-random bit sequences. Based on the use of various forms of the neighborhood for the XOR function, the quality of generators was analyzed using graphical tests and NIST tests. As a result of experimental studies, the optimal dimension of cellular automata and the number of heterogeneous cells were determined, which make it possible to obtain a high-quality pseudo-random bit sequence. The obtained results allowed to formulate a method for constructing high-quality pseudo-random number generators based on cellular automata, as well as to determine the necessary initial conditions for generators. The proposed generators allow to increase the length of the repetition period of a pseudo-random bit sequence.

Pseudorandom Number Generators Based on Asynchronous Cellular Automata

2017 ◽  
pp. 66-108
Keyword(s):  
Cellular Automata ◽  
Cellular Automaton ◽  
Random Number ◽  
Random Number Generator ◽  
Active Cell ◽  
Time Step ◽  
Random Number Generators ◽  
Von Neumann ◽  
Asynchronous Cellular Automata ◽  
Pseudo Random Number

The fourth chapter deals with the use of asynchronous cellular automata for constructing high-quality pseudo-random number generators. A model of such a generator is proposed. Asynchronous cellular automata are constructed using the neighborhood of von Neumann and Moore. Each cell of such an asynchronous cellular state can be in two states (information and active states). There is only one active cell at each time step in an asynchronous cellular automaton. The cell performs local functions only when it is active. At each time step, the active cell transmits its active state to one of the neighborhood cells. An algorithm for the operation of a pseudo-random number generator based on an asynchronous cellular automaton is described, as well as an algorithm for working a cell. The hardware implementation of such a generator is proposed. Several variants of cell construction are considered.

Sign in / Sign up

Export Citation Format

Share Document