Elementary Cellular Automata with Minimal Memory and Random Number Generation

Alonso‐Sanz, Ramón; Bull, Larry

doi:10.25088/complexsystems.18.2.195

Cited by 8 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MECOMP.NET shows the potential to go beyond classical bio-computing strategies such as self-reproducing machines [5], cellular automata [6,7], multilayer perceptrons and neural networks [8,9], genetic algorithms [10,11], adaptive computing [12], bacteriabased computation [13,14], and artificial cells [15]. Interestingly, these models are not just speculative or hypothetical, the state-of-the-art in this proposal shows a large number of studies that solidly support the possibility of creating such systems.…”

Section: Introductionmentioning

confidence: 89%

An In Vivo Proposal of Cell Computing Inspired by Membrane Computing

et al. 2021

View full text Add to dashboard Cite

Intractable problems are challenging and not uncommon in Computer Science. The computing generation we are living in forces us to look for an alternative way of computing, as current computers are facing limitations when dealing with complex problems and bigger input data. Physics and Biology offer great alternatives to solve these problems that traditional computers cannot. Models like Quantum Computing and cell computing are emerging as possible solutions to the current problems the conventional computers are facing. This proposal describes an in vivo framework inspired by membrane computing and based on alternative computational frameworks that have been proven to be theoretically correct such as chemical reaction series. The abilities of a cell as a computational unit make this proposal a starting point in the creation of feasible potential frameworks to enhance the performance of applications in different disciplines such as Biology, BioMedicine, Computer networks, and Social Sciences, by accelerating drastically the way information is processed by conventional architectures and possibly achieving results that presently are not possible due to the limitations of the current computing paradigm. This paper introduces an in vivo solution that uses the principles of membrane computing and it can produce non-deterministic outputs.

show abstract

Section: Introductionmentioning

confidence: 89%

An In Vivo Proposal of Cell Computing Inspired by Membrane Computing

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Sometime whole configurations of a finite CA are also considered as numbers. Some other works of generating pseudo-random numbers are Tomassini et al (2000); Alonso-Sanz and Bull (2009). In Sipper and Tomassini (1996); Wang et al (2008), some optimization algorithms are applied to CAs, whereas in Guan and Zhang (2003); Guan and Tan (2004) dynamic behavior is allowed in the cells to generate the pseudo-random numbers.…”

Section: Randomnessmentioning

confidence: 99%

A survey of cellular automata: types, dynamics, non-uniformity and applications

et al. 2018

View full text Add to dashboard Cite

Cellular automata (CAs) are dynamical systems which exhibit complex global behavior from simple local interaction and computation. Since the inception of cellular automaton (CA) by von Neumann in 1950s, it has attracted the attention of several researchers over various backgrounds and fields for modelling different physical, natural as well as reallife phenomena. Classically, CAs are uniform. However, non-uniformity has also been introduced in update pattern, lattice structure, neighborhood dependency and local rule. In this survey, we tour to the various types of CAs introduced till date, the different characterization tools, the global behaviors of CAs, like universality, reversibility, dynamics etc. Special attention is given to non-uniformity in CAs and especially to non-uniform elementary CAs, which have been very useful in solving several real-life problems.

show abstract

“…For example, in [68], an algorithm is given to select a non-uniform non-linear CA as the random number generator. Some other works of using hybrid CAs are [65,70,86]. In [86], cells of the CAs were allowed to hold memory of their last two state values.…”

Section: Outputmentioning

confidence: 99%

“…Some other works of using hybrid CAs are [65,70,86]. In [86], cells of the CAs were allowed to hold memory of their last two state values. Here, numbers were taken from overlapping window of size 50 and the CAs are with rules 30, 90 or 150.…”

Section: Outputmentioning

confidence: 99%

A Search for Good Pseudo-random Number Generators : Survey and Empirical Studies

Bhattacharjee¹,

Maity²,

Das³

2018

Preprint

View full text Add to dashboard Cite

In today's world, several applications demand numbers which appear random but are generated by a background algorithm; that is, pseudo-random numbers. Since late 19 th century, researchers have been working on pseudo-random number generators (PRNGs). Several PRNGs continue to develop, each one demanding to be better than the previous ones. In this scenario, this paper targets to verify the claim of so-called good generators and rank the existing generators based on strong empirical tests in same platforms. To do this, the genre of PRNGs developed so far has been explored and classified into three groups -linear congruential generator based, linear feedback shift register based and cellular automata based. From each group, well-known generators have been chosen for empirical testing. Two types of empirical testing has been done on each PRNG -blind statistical tests with Diehard battery of tests, TestU01 library and NIST statistical test-suite and graphical tests (lattice test and spacetime diagram test). Finally, the selected 29 PRNGs are divided into 24 groups and are ranked according to their overall performance in all empirical tests.

show abstract

Elementary Cellular Automata with Minimal Memory and Random Number Generation

Cited by 8 publications

References 18 publications

An In Vivo Proposal of Cell Computing Inspired by Membrane Computing

An In Vivo Proposal of Cell Computing Inspired by Membrane Computing

A survey of cellular automata: types, dynamics, non-uniformity and applications

A Search for Good Pseudo-random Number Generators : Survey and Empirical Studies

Contact Info

Product

Resources

About