scholarly journals INCREMENTAL EVOLUTION OF CELLULAR AUTOMATA FOR RANDOM NUMBER GENERATION

2003 ◽  
Vol 14 (07) ◽  
pp. 881-896 ◽  
Author(s):  
SHENG-UEI GUAN ◽  
SHU ZHANG
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Structural Complexity ◽  
Random Number Generation ◽  
Number Generation ◽  
Multi Objective Genetic Algorithm ◽  
Incremental Evolution ◽  
Number Sequences ◽  
Output Efficiency ◽  
Pseudorandom Number Generation

Cellular automata (CA) have been used in pseudorandom number generation for over a decade. Recent studies show that controllable CA (CCA) can generate better random sequences than conventional one-dimensional (1D) CA and compete with two-dimensional (2D) CA. Yet the structural complexity of CCA is higher than that of 1D programmable cellular automata (PCA). It would be good if CCA can attain a good randomness quality with the least structural complexity. In this paper, we evolve PCA/CCA to their lowest complexity level using genetic algorithms (GAs). Meanwhile, the randomness quality and output efficiency of PCA/CCA are also evolved. The evolution process involves two algorithms — a multi-objective genetic algorithm (MOGA) and an algorithm for incremental evolution. A set of PCA/CCA are evolved and compared in randomness, complexity, and efficiency. The results show that without any spacing, the CCA could generate good random number sequences that could pass DIEHARD. To obtain the same randomness quality, the structural complexity of the CCA is not higher than that of 1D CA. Furthermore, the methodology developed could be used to evolve other CA or serve as a yardstick to compare different types of CA.

scholarly journals CONFIGURABLE CELLULAR AUTOMATA FOR PSEUDORANDOM NUMBER GENERATION

2005 ◽  
Vol 16 (07) ◽  
pp. 1051-1073 ◽  
Author(s):  
MARIE THERESE QUIETA ◽  
SHENG-UEI GUAN
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Random Number Generation ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generator ◽  
New Approach ◽  
Suitable Candidate ◽  
Number Generation ◽  
A Cell ◽  
Pseudorandom Number Generation

This paper proposes a generalized structure of cellular automata (CA) — the configurable cellular automata (CoCA). With selected properties from programmable CA (PCA) and controllable CA (CCA), a new approach to cellular automata is developed. In CoCA, the cells are dynamically reconfigured at run-time via a control CA. Reconfiguration of a cell simply means varying the properties of that cell with time. Some examples of properties to be reconfigured are rule selection, boundary condition, and radius. While the objective of this paper is to propose CoCA as a new CA method, the main focus is to design a CoCA that can function as a good pseudorandom number generator (PRNG). As a PRNG, CoCA can be a suitable candidate as it can pass 17 out of 18 Diehard tests with 31 cells. CoCA PRNG's performance based on Diehard test is considered superior over other CA PRNG works. Moreover, CoCA opens new rooms for research not only in the field of random number generation, but in modeling complex systems as well.

Parallel random number generation for VLSI systems using cellular automata

1989 ◽  
Vol 38 (10) ◽  
pp. 1466-1473 ◽  
Author(s):  
P.D. Hortensius ◽  
R.D. McLeod ◽  
H.C. Card
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Random Number Generation ◽  
Number Generation

scholarly journals Twister Generator of Stochastic Planes

2019 ◽  
pp. 27-45
Author(s):  
A.F. Deon ◽  
V.A. Onuchin ◽  
Yu.A. Menyaev
Keyword(s):  
Random Number ◽  
Random Access ◽  
Random Number Generation ◽  
Random Numbers ◽  
Access Memory ◽  
Number Generation ◽  
Novel Approach ◽  
Pseudorandom Number Generation ◽  
Sequence Parameters ◽  
Uniform Random Numbers

Various pseudorandom number generation algorithms may be used to create a discrete stochastic plane. If a Cartesian completeness property is required of the plane, it must be uniform. The point is, employing the concept of uncontrolled random number generation may yield low-quality results, since original sequences may omit random numbers or not be sufficiently uniform. We present a novel approach for generating stochastic Cartesian planes according to the model of complete twister sequences featuring uniform random numbers without omissions or repetitions. Simulation results confirm that the random planes obtained are indeed perfectly uniform. Moreover, recombining the original complete uniform sequence parameters allows the number of planes created to be significantly increased without using any extra random access memory.

RANDOM NUMBER GENERATION BY CELLULAR AUTOMATA WITH MEMORY

2008 ◽  
Vol 19 (02) ◽  
pp. 351-367 ◽  
Author(s):  
RAMÓN ALONSO-SANZ ◽  
LARRY BULL
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Random Number Generation ◽  
Random Number Generators ◽  
Potential Value ◽  
Number Generation ◽  
Elementary Cellular Automata ◽  
With Memory ◽  
Standard Framework

This paper considers an extension to the standard framework of cellular automata which implements memory capabilities by featuring cells by elementary rules of its last three states. A study is made of the potential value of elementary cellular automata with elementary memory rules as random number generators.

scholarly journals A cognitive fingerprint in human random number generation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marc-André Schulz ◽  
Sebastian Baier ◽  
Benjamin Timmermann ◽  
Danilo Bzdok ◽  
Karsten Witt
Keyword(s):  
Cognitive Process ◽  
Random Number ◽  
Edit Distance ◽  
Individual Preference ◽  
Random Number Generation ◽  
Random Generation ◽  
Number Generation ◽  
Number Sequences ◽  
Healthy Participants

AbstractIs the cognitive process of random number generation implemented via person-specific strategies corresponding to highly individual random generation behaviour? We examined random number sequences of 115 healthy participants and developed a method to quantify the similarity between two number sequences on the basis of Damerau and Levenshtein’s edit distance. “Same-author” and “different author” sequence pairs could be distinguished (96.5% AUC) based on 300 pseudo-random digits alone. We show that this phenomenon is driven by individual preference and inhibition of patterns and stays constant over a period of 1 week, forming a cognitive fingerprint.

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