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

Genetic Drift03:33

Genetic Drift

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.Life is not fair. A deer grazing contentedly in a field can have her meal cut tragically short by a bolt of lightning. If the doomed doe is one of only three in the population, 1/3 of the population’s gene pool is lost. Random events like this can...
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

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).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In-vitro Mutagenesis01:16

In-vitro Mutagenesis

To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
Evolutionary Processes in Microbes01:26

Evolutionary Processes in Microbes

Microbial evolution occurs rapidly due to short generation times and a variety of genetic processes, including horizontal gene transfer, mutation, recombination, and genetic drift. These mechanisms collectively enable microbes to adapt swiftly to changing environments.Horizontal gene transfer (HGT) allows genes to move between different species and occurs through three main mechanisms: conjugation, transformation, and transduction. Conjugation involves direct cell-to-cell contact for DNA...

You might also read

Related Articles

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

Sort by
Same author

Structural properties, polymorphism, and multiscale disorder unravel energy transport limitations in perylene diimide semiconductors.

Science advances·2026
Same author

Harnessing microalgae for the biosynthesis of molecular crystals.

Nature biotechnology·2026
Same author

Understanding the Mechanism of Nontraditional Zeolite Synthesis Using <i>In Situ</i> Nuclear Magnetic Resonance Spectroscopy and X-ray Diffraction.

Journal of the American Chemical Society·2025
Same author

In-situ solid-state NMR spectroscopy reveals competing crystallization pathways for a system that forms structurally diverse multicomponent crystalline phases.

Solid state nuclear magnetic resonance·2025
Same author

On-site genetic diagnosis for the invasive pest Hylurgus ligniperda (Fabricius) and its possible application.

Pest management science·2025
Same author

Exploiting <i>in situ</i> NMR spectroscopy to understand non-traditional methods for zeolite synthesis.

Chemical science·2025

Related Experiment Video

Updated: Jun 27, 2026

Generation of Genetically Modified Mice through the Microinjection of Oocytes
10:19

Generation of Genetically Modified Mice through the Microinjection of Oocytes

Published on: June 15, 2017

Counteracting stagnation in genetic algorithm calculations by implementation of a micro genetic algorithm strategy.

Zhongfu Zhou1, Kenneth D M Harris

  • 1School of Chemistry, Cardiff University, Park Place, Cardiff, CF10 3AT, Wales.

Physical Chemistry Chemical Physics : PCCP
|December 9, 2008
PubMed
Summary

A novel micro genetic algorithm (GA) strategy enhances standard GAs by detecting and resolving population stagnation. This approach improves evolutionary progress and structural diversity, particularly in powder X-ray diffraction structure solution.

More Related Videos

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat
06:03

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

Published on: September 20, 2016

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

Related Experiment Videos

Last Updated: Jun 27, 2026

Generation of Genetically Modified Mice through the Microinjection of Oocytes
10:19

Generation of Genetically Modified Mice through the Microinjection of Oocytes

Published on: June 15, 2017

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat
06:03

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

Published on: September 20, 2016

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

Area of Science:

  • Computational chemistry
  • Crystallography
  • Evolutionary computation

Background:

  • Standard genetic algorithms (GAs) can suffer from population stagnation, hindering optimization progress.
  • Direct-space structure solution from powder X-ray diffraction data is a computationally challenging problem often addressed by GAs.

Purpose of the Study:

  • To introduce a "micro genetic algorithm" (micro-GA) strategy integrated into standard GA procedures.
  • To enhance the ability of GAs to overcome stagnation and improve evolutionary progress.

Main Methods:

  • A stagnation detection criterion is applied to standard GA populations.
  • A micro-GA procedure is triggered upon stagnation detection, involving parallel evolution of small sub-populations.
  • Sub-populations comprise new random structures and elite individuals from the stagnated population.

Main Results:

  • The micro-GA strategy effectively promotes structural diversity in stagnated populations.
  • Significantly improved evolutionary progress was observed in direct-space structure solution from powder X-ray diffraction data.
  • The strategy demonstrated efficacy in overcoming GA stagnation.

Conclusions:

  • The proposed micro-GA strategy is an effective method for alleviating stagnation in GA calculations.
  • This approach shows potential for broader applicability in various fields relying on GA optimization.
  • The integration enhances the robustness and performance of genetic algorithms.