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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

97
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...
97
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

2.9K
2.9K
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

2.5K
2.5K
Genetic Variation01:25

Genetic Variation

355
Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
355
Genetic Drift03:33

Genetic Drift

40.5K
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.
40.5K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

59.2K
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).
59.2K

You might also read

Related Articles

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

Sort by
Same author

<i>SocialViruses</i>: integrating quantitative phage-bacteria and phage-phage interaction networks for rational cocktail design.

Bioinformatics advances·2025
Same author

Retraction of Deficient LRRC8A-dependent volume-regulated anion channel activity is associated with male infertility in mice.

JCI insight·2025
Same author

Reference Vector-guided Evolutionary Algorithm for cluster analysis of single-cell transcriptomes.

Computer methods and programs in biomedicine·2025
Same author

A protein-protein interaction network aligner study in the multi-objective domain.

Computer methods and programs in biomedicine·2024
Same author

Iterative Level-0: A new and fast algorithm to traverse mating networks calculating the inbreeding and relationship coefficients.

Computers in biology and medicine·2023
Same author

Theory and practice of natural computing: tenth edition.

Neural computing & applications·2022
Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·2026
Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
Same journal

Digital Redesign-Based Interval State Estimation for Continuous Systems With Aperiodic Discrete Measurements.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Aug 19, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K

Automatic Update Summarization by a Multiobjective Number-One-Selection Genetic Approach.

Jesus M Sanchez-Gomez, Miguel A Vega-Rodriguez, Carlos J Perez

    IEEE Transactions on Cybernetics
    |December 1, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel algorithm for update summarization, focusing on dynamic information. The multiobjective number-one-selection genetic algorithm (MONOGA) effectively identifies and summarizes new information for users, outperforming existing methods.

    More Related Videos

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    7.6K
    A Quantitative Fitness Analysis Workflow
    11:39

    A Quantitative Fitness Analysis Workflow

    Published on: August 13, 2012

    14.6K

    Related Experiment Videos

    Last Updated: Aug 19, 2025

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
    11:53

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

    Published on: December 9, 2012

    13.0K
    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    7.6K
    A Quantitative Fitness Analysis Workflow
    11:39

    A Quantitative Fitness Analysis Workflow

    Published on: August 13, 2012

    14.6K

    Area of Science:

    • Natural Language Processing
    • Artificial Intelligence
    • Information Retrieval

    Background:

    • The internet's exponential data growth necessitates efficient automatic text summarization.
    • Update summarization addresses dynamic document collections, unlike static traditional summarization.
    • The challenge lies in summarizing new information for users already familiar with previous content.

    Purpose of the Study:

    • To design and implement an algorithm for effective update summarization.
    • To produce summaries that are relevant to user queries and highlight new information.
    • To improve the performance of automatic summarization systems in dynamic environments.

    Main Methods:

    • Development and implementation of the multiobjective number-one-selection genetic algorithm (MONOGA).
    • Evaluation using Text Analysis Conference (TAC) datasets.
    • Performance assessment via Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics.

    Main Results:

    • The proposed MONOGA algorithm successfully generates relevant and update-focused summaries.
    • Experimental results demonstrate significant improvements over existing approaches.
    • Average percentage improvements in ROUGE scores ranged from 12.74% to 55.03%.

    Conclusions:

    • MONOGA is a highly effective approach for the challenging task of update summarization.
    • The algorithm offers a substantial advancement in summarizing dynamic information.
    • This work contributes to more efficient information retrieval in rapidly evolving data landscapes.