Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.5K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.5K
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

8.4K
While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
8.4K
What is Population Genetics?01:25

What is Population Genetics?

59.9K
A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
59.9K
Genetic Drift03:33

Genetic Drift

40.9K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
40.9K
Randomized Experiments01:13

Randomized Experiments

7.9K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.9K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

59.6K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
59.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Do I Have Your Attention Now? Supporting Attention in Dynamic Virtual Environments.

IEEE transactions on visualization and computer graphics·2026
Same author

Implementing Health-Related Quality of Life Assessment in Pediatric Oncology: A Feasibility Study.

Pediatric blood & cancer·2026
Same author

Breaking the cycle: addressing the evolving dynamic of violence associated with the European drug market.

The International journal on drug policy·2026
Same author

Predictive Models for Kidney Offer Acceptance: Challenges and Strategies.

Journal of transplantation·2026
Same author

The Biodiversity of Peter I Island-The Most Remote Island in the World.

Ecology and evolution·2025
Same author

Meta-learner-based frameworks for interpretable email spam detection.

Frontiers in artificial intelligence·2025

Related Experiment Video

Updated: Sep 22, 2025

Author Spotlight: Advancing Protein Engineering – Harnessing Evolution Through PRANCE and Lab Automation
05:08

Author Spotlight: Advancing Protein Engineering – Harnessing Evolution Through PRANCE and Lab Automation

Published on: January 12, 2024

1.8K

Design of a cryptographically secure pseudo random number generator with grammatical evolution.

Conor Ryan1, Meghana Kshirsagar2, Gauri Vaidya3

  • 1Biocomputing and Developmental Systems Group, Lero, The Science Foundation Ireland Research Centre for Software, Computer Science and Information System Department, University of Limerick, Limerick, V94 T9PX, Ireland, UK. conor.ryan@ul.ie.

Scientific Reports
|May 21, 2022
PubMed
Summary

Grammatical Evolution (GE) effectively generates seeds for pseudo-random number generators (PRNGs) and cryptographically secure PRNGs. These GE-generated seeds yield high entropy, ensuring robust random number generation for applications like pi estimation and cryptographic security.

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.1K
Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection
10:50

Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection

Published on: September 27, 2016

9.8K

Related Experiment Videos

Last Updated: Sep 22, 2025

Author Spotlight: Advancing Protein Engineering – Harnessing Evolution Through PRANCE and Lab Automation
05:08

Author Spotlight: Advancing Protein Engineering – Harnessing Evolution Through PRANCE and Lab Automation

Published on: January 12, 2024

1.8K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.1K
Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection
10:50

Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection

Published on: September 27, 2016

9.8K

Area of Science:

  • Computational Intelligence
  • Cryptography
  • Algorithm Design

Background:

  • Pseudo-random number generators (PRNGs) are crucial for simulations and cryptography.
  • Ensuring the quality and security of random number generation is a persistent challenge.
  • Grammatical Evolution (GE) offers a novel approach to program synthesis and optimization.

Purpose of the Study:

  • To investigate the use of Grammatical Evolution (GE) for generating initial seeds for PRNGs and cryptographically secure (CS) PRNGs.
  • To evaluate the entropy quality of GE-generated seeds.
  • To develop and assess the performance of GE-based random number generators.

Main Methods:

  • Utilized Grammatical Evolution (GE) to evolve initial seeds for PRNGs.
  • Quantified seed entropy using an 8-bit entropy measure.
  • Developed GE-PRNG and GE-CSPRNG, incorporating GE-generated seeds.
  • Employed Monte Carlo simulations for GE-PRNG efficacy testing (pi estimation).
  • Applied National Institute of Standards and Technology (NIST) SP800-22 and Diehard tests for GE-CSPRNG validation.
  • Conducted computational performance analysis for GE-CSPRNG.

Main Results:

  • GE seeds demonstrated high average entropy (7.940560934 out of 8 bits).
  • GE-PRNG accurately estimated pi to six decimal places using 100 million random numbers.
  • GE-CSPRNG met NIST SP800-22 and Diehard statistical test requirements.
  • GE-CSPRNG exhibited potential for industrial applications based on performance analysis.

Conclusions:

  • Grammatical Evolution is a suitable entropy source for generating high-quality PRNG seeds.
  • The developed GE-PRNG and GE-CSPRNG are effective and meet rigorous statistical standards.
  • GE-CSPRNG shows promise for practical implementation in industrial settings requiring secure random number generation.