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

175
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...
175
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.6K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.6K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

60.8K
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).
60.8K
Evolutionary Psychology01:20

Evolutionary Psychology

661
Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
661
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.7K
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...
7.7K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

3.2K
3.2K

You might also read

Related Articles

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

Sort by
Same author

Knockdown of SIK3 in the CA1 Region can Reduce Seizure Susceptibility in Mice by Inhibiting Decreases in GABA<sub>A</sub>R α1 Expression.

Molecular neurobiology·2023
Same author

ROS-mediated waterlogging memory, induced by priming, mitigates photosynthesis inhibition in tomato under waterlogging stress.

Frontiers in plant science·2023
Same author

Commentary: Copper and cuproptosis-related genes in hepatocellular carcinoma: therapeutic biomarkers targeting tumor immune microenvironment and immune checkpoints.

Frontiers in immunology·2023
Same author

Discovery of a three-proton insertion mechanism in α-molybdenum trioxide leading to enhanced charge storage capacity.

Nature communications·2023
Same author

Dynamic simulation of carbon emission under different policy scenarios in Pearl River Delta urban agglomeration, China.

Environmental science and pollution research international·2023
Same author

ABC transporter-mediated MXR mechanism in fish embryos and its potential role in the efflux of nanoparticles.

Ecotoxicology and environmental safety·2023
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: Nov 18, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.2K

Enhanced Multifactorial Evolutionary Algorithm With Meme Helper-Tasks.

Xiaoliang Ma, Jian Yin, Anmin Zhu

    IEEE Transactions on Cybernetics
    |February 10, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Evolutionary multitasking (EMT) enhances optimization by transferring knowledge between tasks. A new method, MVD-enhanced MFEA, improves knowledge transfer, boosting performance on complex problems.

    More Related Videos

    A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
    07:09

    A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

    Published on: May 28, 2021

    10.1K
    Automated Microbial Cultivation and Adaptive Evolution using Microbial Microdroplet Culture System MMC
    08:18

    Automated Microbial Cultivation and Adaptive Evolution using Microbial Microdroplet Culture System MMC

    Published on: February 18, 2022

    4.2K

    Related Experiment Videos

    Last Updated: Nov 18, 2025

    Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
    08:57

    Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

    Published on: August 14, 2018

    16.2K
    A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
    07:09

    A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

    Published on: May 28, 2021

    10.1K
    Automated Microbial Cultivation and Adaptive Evolution using Microbial Microdroplet Culture System MMC
    08:18

    Automated Microbial Cultivation and Adaptive Evolution using Microbial Microdroplet Culture System MMC

    Published on: February 18, 2022

    4.2K

    Area of Science:

    • Evolutionary computation
    • Artificial intelligence
    • Optimization algorithms

    Background:

    • Evolutionary multitasking (EMT) aims to solve multiple optimization tasks simultaneously.
    • Knowledge transfer between tasks is crucial for EMT effectiveness.
    • Existing multifactorial evolutionary algorithms (MFEA) can suffer from ineffective or negative knowledge transfer.

    Purpose of the Study:

    • To improve the performance of MFEA by addressing ineffective intertask knowledge transfer.
    • To introduce a novel approach using prior-knowledge-based multiobjectivization via decomposition (MVD).
    • To construct strongly related meme helper-tasks for enhanced positive knowledge transfer.

    Main Methods:

    • Incorporating MVD into MFEA to create related multiobjective optimization problems.
    • Utilizing problem structure or decision variable grouping to form helper-tasks.
    • Reducing local optima and increasing population diversity through MVD.

    Main Results:

    • The proposed MVD-enhanced MFEA demonstrates improved performance compared to standard MFEA.
    • Constructed meme helper-tasks effectively leverage prior knowledge of target problems.
    • Experiments on standard test problems validate the enhanced intertask knowledge transfer.

    Conclusions:

    • The MVD-enhanced MFEA approach successfully improves knowledge transfer in evolutionary multitasking.
    • This method offers a promising direction for enhancing the efficiency and effectiveness of EMT.
    • Prior-knowledge-based decomposition is a viable strategy for optimizing MFEA performance.