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

376
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...
376
Types of Selection01:46

Types of Selection

45.9K
Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
45.9K
Inclusive Fitness00:57

Inclusive Fitness

43.2K
Most altruistic behavior—in which one animal helps another at a cost to themselves—occurs between relatives. Scientists think these altruistic behaviors evolved because they increase the inclusive fitness of the animal providing help.
43.2K
Limits to Natural Selection01:38

Limits to Natural Selection

35.7K
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.
35.7K
Multiple Allele Traits01:49

Multiple Allele Traits

38.5K
The Concept of Multiple Allelism
38.5K
Frequency-dependent Selection01:21

Frequency-dependent Selection

24.4K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
24.4K

You might also read

Related Articles

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

Sort by
Same author

A V<sub>2</sub>CT<sub>x</sub>/V<sub>2</sub>O<sub>5</sub>/SnO<sub>2</sub> Ternary Heterostructure<i>-</i>Based Gas Sensor for Highly Selective Detection of Electrolyte Leakage in a Lithium<i>-</i>Ion Battery.

ACS sensors·2026
Same author

A Multiobjective Evolutionary Algorithm Based on Bipopulation With Uniform Sampling for Neural Architecture Search.

IEEE transactions on neural networks and learning systems·2026
Same author

A Time-Division-Based Constrained Multiobjective Optimization Method for Coal Mine Integrated Energy System Dispatch Problem.

IEEE transactions on cybernetics·2026
Same author

Model-free and finite-time sliding-mode tracking control based on a second-order adaptive disturbance observer.

ISA transactions·2025
Same author

Dynamic Ensemble Selection for Imbalanced Data Streams With Concept Drift.

IEEE transactions on neural networks and learning systems·2022
Same author

Multisurrogate-Assisted Multitasking Particle Swarm Optimization for Expensive Multimodal Problems.

IEEE transactions on cybernetics·2021
Same journal

Relaxed Stability Conditions for Model Predictive Control of Hybrid Dynamical Systems Using Hybrid Recurrent Neural Networks.

IEEE transactions on cybernetics·2026
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
See all related articles

Related Experiment Video

Updated: Mar 8, 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

13.6K

A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy.

Yiping Liu, Dunwei Gong, Jing Sun

    IEEE Transactions on Cybernetics
    |January 17, 2017
    PubMed
    Summary
    This summary is machine-generated.

    A new many-objective evolutionary algorithm improves performance by selecting individuals one by one to enhance convergence and diversity. This approach effectively addresses challenges in high-dimensional objective spaces for optimization problems.

    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

    8.1K
    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
    11:53

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    12.3K

    Related Experiment Videos

    Last Updated: Mar 8, 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

    13.6K
    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

    8.1K
    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
    11:53

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    12.3K

    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Evolutionary Computation

    Background:

    • Many-objective optimization problems (MaOPs) pose significant challenges for existing evolutionary algorithms.
    • Difficulty in balancing convergence and diversity within high-dimensional objective spaces is a key issue.

    Purpose of the Study:

    • To propose a novel many-objective evolutionary algorithm (MaOEAs) designed to overcome limitations of current algorithms.
    • To enhance the ability to balance convergence and diversity in high-dimensional objective spaces.

    Main Methods:

    • A one-by-one selection strategy is introduced for environmental selection, prioritizing convergence.
    • A niche technique using a distribution indicator de-emphasizes neighbors to ensure population diversity.
    • Angle-based similarity measures and corner solutions are employed for effective distribution and handling scaled problems.

    Main Results:

    • The proposed algorithm demonstrated superior performance compared to eight state-of-the-art MaOEAs.
    • Empirical comparisons were conducted on 80 instances across 16 benchmark problems.
    • The algorithm effectively increased selection pressure towards the Pareto optimal front.

    Conclusions:

    • The novel one-by-one selection strategy significantly enhances the performance of many-objective evolutionary algorithms.
    • The proposed algorithm offers a robust solution for tackling complex many-objective optimization problems.
    • The findings suggest a promising direction for future research in evolutionary computation for MaOPs.