Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mismatch Repair

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

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...

Mutation, Gene Flow, and Genetic Drift

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).

Mutations

Mutations

Genetic Drift

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.

Genome Size and the Evolution of New Genes

Genome Size and the Evolution of New Genes

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.

You might also read

Related Articles

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

Sort by

Same author

Low-sampling-rate compressed ghost edge imaging via energy-descending ordered speckle patterns.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026

Same author

Nonzero-mean light off-axis phase imaging based on light intensity correlation.

Optics letters·2026

Same author

Continuous focus field strength adjustment in a scattered field based on the third-order correlation of light fields.

Optics letters·2024

Same author

Low-cost wavefront shaping via the third-order correlation of light fields.

Optics letters·2023

Same author

The electro-optic spatial light modulator of lithium niobate metasurface based on plasmonic quasi-bound states in the continuum.

Nanoscale·2023

Same author

Hybrid resonance metasurface for a lithium niobate electro-optical modulator.

Optics letters·2023

Same journal

Multifunctional reconfigurable terahertz metasurface based on vanadium dioxide phase transition: achieving broadband absorption and efficient polarization conversion.

Applied optics·2026

Same journal

High-Q-factor electromagnetically induced transparency utilizing quasi-bound states in the continuum in an all-dielectric terahertz metasurface.

Applied optics·2026

Same journal

Automated stitching interferometry for high-precision metrology of X-ray mirrors.

Applied optics·2026

Same journal

Experimental demonstration of an approach to designing a metal-dielectric DBR resonant cavity structure.

Applied optics·2026

Same journal

High-precision wavefront reconstruction from a single-shot interferogram using a physics-driven hybrid feature calibration network.

Applied optics·2026

Same journal

Ultra-high-Q Fano resonance based on coupled topological corner states in Kagome photonic crystals.

Applied optics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 6, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Progressive acceleration in the genetic algorithm by intragenerational mutation.

Wenjing He, Wenkang Li, Haoyi Zuo

|January 4, 2024

Summary

This summary is machine-generated.

Intragenerational mutation of the genetic algorithm (IMGA) expands search spaces by varying mutation rates. This optimization method reduces iterations and enhances convergence speed for improved performance.

More Related Videos

Measuring Microbial Mutation Rates with the Fluctuation Assay

Measuring Microbial Mutation Rates with the Fluctuation Assay

Published on: November 28, 2019

Combining Magnetic Sorting of Mother Cells and Fluctuation Tests to Analyze Genome Instability During Mitotic Cell Aging in Saccharomyces cerevisiae

Combining Magnetic Sorting of Mother Cells and Fluctuation Tests to Analyze Genome Instability During Mitotic Cell Aging in Saccharomyces cerevisiae

Published on: October 16, 2014

Related Experiment Videos

Last Updated: Jul 6, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Measuring Microbial Mutation Rates with the Fluctuation Assay

Measuring Microbial Mutation Rates with the Fluctuation Assay

Published on: November 28, 2019

Combining Magnetic Sorting of Mother Cells and Fluctuation Tests to Analyze Genome Instability During Mitotic Cell Aging in Saccharomyces cerevisiae

Combining Magnetic Sorting of Mother Cells and Fluctuation Tests to Analyze Genome Instability During Mitotic Cell Aging in Saccharomyces cerevisiae

Published on: October 16, 2014

Area of Science:

Computational intelligence
Optimization algorithms
Evolutionary computation

Background:

Genetic algorithms (GAs) are widely used for optimization.
Traditional GAs can face challenges with premature convergence and limited search space exploration.
Enhancing the adaptability of GAs is crucial for complex problem-solving.

Purpose of the Study:

To introduce a novel mutation strategy for genetic algorithms.
To improve the efficiency and effectiveness of the optimization process.
To address limitations in search space exploration within genetic algorithms.

Main Methods:

Proposing the intragenerational mutation of the genetic algorithm (IMGA).
Implementing variable mutation rates across individuals within each generation.
Analyzing the impact on search space size and convergence dynamics.

Main Results:

Demonstrated active broadening of the search space during optimization.
Significantly increased the variation of mutation rates among individuals.
Achieved a reduction in the number of required iterations.
Improved convergence speed and the overall enhancement factor.

Conclusions:

IMGA effectively expands the search space, leading to faster optimization.
The proposed mutation strategy enhances the performance of genetic algorithms.
IMGA offers a promising approach for accelerating complex optimization tasks.