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

Modeling with Differential Equations01:25

Modeling with Differential Equations

327
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
327
Population Growth00:57

Population Growth

23.1K
Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
23.1K
Limits to Natural Selection01:38

Limits to Natural Selection

30.0K
Organisms that are well-adapted to their environment are more likely to survive and reproduce. However, natural selection does not lead to perfectly adapted organisms. Several factors constrain natural selection.
30.0K
Conservation of Small Populations02:04

Conservation of Small Populations

14.3K
Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less...
14.3K
Conservation of Declining Populations02:07

Conservation of Declining Populations

11.5K
Conservation of declining population focuses on ways of detecting, diagnosing, and halting a population decline. The approach uses methods to prevent populations from going extinct.
11.5K
Convergent Evolution01:54

Convergent Evolution

27.5K
Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.
27.5K

You might also read

Related Articles

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

Sort by
Same author

Acute effects of anodal transcranial direct current stimulation on whole-body dynamic endurance performance: a systematic review and meta-analysis.

Applied physiology, nutrition, and metabolism = Physiologie appliquee, nutrition et metabolisme·2026
Same author

Caformer: Rethinking Time-Series Forecasting From Causal Perspective.

IEEE transactions on cybernetics·2025
Same author

Imitation Learning for Multiobjective Optimization-AlphaMOEA.

IEEE transactions on cybernetics·2025
Same author

A Systematic Review and Meta-Analysis of the Effectiveness and Cost of Single-Site Robotic Surgery and Single-Site Laparoscopic Surgery in Gynecological Diseases: The Era of Single-Site Robotic Surgery May Have Arrived.

Cancer reports (Hoboken, N.J.)·2025
Same author

REaMA: Building Biomedical Relation Extraction Specialized Large Language Models Through Instruction Tuning.

IEEE transactions on neural networks and learning systems·2025
Same author

Uncovering Large Language Model Weaknesses in Character and Word Understanding and Manipulating.

IEEE transactions on neural networks and learning systems·2025
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: Apr 25, 2026

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

12.6K

A dual-population differential evolution with coevolution for constrained optimization.

Wei-Feng Gao, Gary G Yen, San-Yang Liu

    IEEE Transactions on Cybernetics
    |August 20, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a dual-population differential evolution (DPDE) algorithm for constrained optimization problems. DPDE effectively handles complex optimization tasks by dividing labor and fostering cooperation between subpopulations.

    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

    1.2K
    Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
    15:00

    Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

    Published on: August 18, 2023

    5.1K

    Related Experiment Videos

    Last Updated: Apr 25, 2026

    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

    12.6K
    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

    1.2K
    Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
    15:00

    Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

    Published on: August 18, 2023

    5.1K

    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Operations Research

    Background:

    • Modern problem-solving relies heavily on team cooperation and division of labor.
    • Constrained optimization problems (COPs) are prevalent in various scientific and engineering domains.
    • Existing constraint-handling algorithms may not fully leverage cooperative strategies.

    Purpose of the Study:

    • To propose a novel dual-population differential evolution (DPDE) algorithm with coevolution for COPs.
    • To adapt the principles of team cooperation and division of labor to evolutionary computation.
    • To enhance the effectiveness of evolutionary algorithms in solving complex optimization problems with constraints.

    Main Methods:

    • Treated COPs as bi-objective problems: optimizing the primary function and constraint violations.
    • Implemented a dual-population strategy, dividing the population based on solution feasibility.
    • Incorporated an information-sharing mechanism between subpopulations for cooperative search.

    Main Results:

    • The proposed DPDE algorithm demonstrated competitive performance on benchmark functions.
    • DPDE effectively optimized both the objective function and constraint satisfaction.
    • The coevolutionary approach with divided labor proved effective for COPs.

    Conclusions:

    • DPDE offers a promising and effective approach for tackling constrained optimization problems.
    • The strategy of dividing labor and information sharing enhances evolutionary search.
    • This method provides a competitive alternative to existing state-of-the-art constraint-handling algorithms.