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

What is Evolutionary History?02:35

What is Evolutionary History?

43.4K
Scientists record evolutionary history by analyzing fossil, morphological, and genetic data. The fossil record documents the history of life on Earth and provides evidence for evolution. However, both fossil and living organisms offer evidence that outlines Earth’s evolutionary history.
43.4K
Synthesis and Decomposition Reactions02:17

Synthesis and Decomposition Reactions

38.2K
Synthesis and decomposition are two types of redox reactions. Synthesis means to make something, whereas decomposition means to break something. The reactions are accompanied by chemical and energy changes. 
38.2K
Evolutionary Psychology01:20

Evolutionary Psychology

1.0K
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...
1.0K
Criticisms of the Evolutionary Perspective01:23

Criticisms of the Evolutionary Perspective

375
In a study where individuals posing as strangers offered compliments and proposed casual sex to students, the responses differed significantly based on gender. Not a single woman accepted the proposal, while 70% of the men agreed. This outcome provides a useful scenario to explore through the lens of evolutionary psychology and social learning theory, highlighting the diverse perspectives on human sexual behaviors.
Evolutionary psychology provides one explanation for these findings, suggesting...
375
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

409
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
409
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

7.0K
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...
7.0K

You might also read

Related Articles

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

Sort by
Same author

Fast funnel control based fixed-time consensus tracking for nonlinear multi-agent systems with unknown disturbances.

ISA transactions·2026
Same author

Robust Nonfragile Consensus Control of MASs With Controller Gain Perturbations and Switching Directed Networks.

IEEE transactions on cybernetics·2025
Same author

Small Molecule Compound DHPA Screened by Computer-Aided Drug Design and Molecular Dynamics Simulation Inhibits Neuroblastoma Cell Proliferation by Targeting TrkB.

ACS omega·2024
Same author

Finite-Time Neuroadaptive Cooperative Control for Nonlinear Multiagent Systems Under Nonaffine Faults and Partially Unknown Control Directions.

IEEE transactions on cybernetics·2024
Same author

Automated Design of Collaboration-Based Hybrid Metaheuristics.

IEEE transactions on cybernetics·2024
Same author

Novel compound heterozygous mutations of the FBP1 gene in a patient with hypoglycemia and lactic acidosis: A case report.

Molecular genetics & genomic medicine·2023
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
Same journal

Output Prediction-Based Event-Triggered Interval Estimation for Continuous-Time Switched Systems.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Feb 1, 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

13.5K

Noise-Tolerant Techniques for Decomposition-Based Multiobjective Evolutionary Algorithms.

Juan Li, Bin Xin, Jie Chen

    IEEE Transactions on Cybernetics
    |December 12, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces noise-tolerant decomposition-based multiobjective evolutionary algorithms (NT-DMOEAs) to address optimization challenges in noisy environments. NT-DMOEAs demonstrate superior performance over existing algorithms on benchmark problems with varying noise levels.

    More Related Videos

    Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology
    12:29

    Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology

    Published on: May 3, 2017

    11.1K
    Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
    07:05

    Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

    Published on: February 15, 2022

    2.9K

    Related Experiment Videos

    Last Updated: Feb 1, 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

    13.5K
    Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology
    12:29

    Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology

    Published on: May 3, 2017

    11.1K
    Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
    07:05

    Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

    Published on: February 15, 2022

    2.9K

    Area of Science:

    • Computational intelligence
    • Optimization algorithms
    • Evolutionary computation

    Background:

    • Decomposition-based multiobjective evolutionary algorithms (DMOEAs) are mainstream for multiobjective optimization.
    • Limited research exists on applying DMOEAs to uncertain or noisy environments.
    • Uncertainty is typically modeled as additive noise in the objective space.

    Purpose of the Study:

    • To investigate the impact of noisy environments on DMOEAs.
    • To propose novel noise-handling techniques for DMOEAs.
    • To develop noise-tolerant DMOEAs (NT-DMOEAs) that maintain performance under uncertainty.

    Main Methods:

    • Empirical analysis of DMOEA performance in noisy conditions.
    • Development of four noise-handling techniques: Pareto-based nadir point estimation, adaptive sampling, mixed objective evaluation, and mixed repair mechanisms.
    • Integration of these techniques into MOEA/D and DMOEA- [Formula: see text] to create NT-DMOEAs.

    Main Results:

    • NT-DMOEAs exhibit significant advantages over existing algorithms on 17 benchmark problems with varying noise intensities.
    • The proposed noise-handling strategies effectively alleviate noise effects and preserve decision-space diversity.
    • NT-DMOEA- [Formula: see text] shows particular superiority among the developed noise-tolerant algorithms.

    Conclusions:

    • The proposed noise-handling techniques enhance the robustness of DMOEAs in uncertain environments.
    • NT-DMOEAs offer a promising approach for multiobjective optimization under noise.
    • Further research can explore the application of NT-DMOEAs in more complex uncertain scenarios.