Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Optimal Foraging

Optimal Foraging

How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.

Optimization Problems

Optimization Problems

Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

You might also read

Related Articles

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

Sort by

Same authorSame journal

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

IEEE transactions on cybernetics·2026

Same author

Local Surrogate Models With Residual Fuzzy Rules for Model-Agnostic Explanations.

IEEE transactions on cybernetics·2026

Same author

A Prediction Model Integrating Adaptive-Network-Based Fuzzy Inference System and Fuzzy C-Mean Clustering.

IEEE transactions on cybernetics·2026

Same author

Individual Linguistic Granular Computing: A Granulation-Degranulation-Based Approach.

IEEE transactions on cybernetics·2026

Same author

S<sup>2</sup>FS: Spatially-Aware Separability-Driven Feature Selection in Fuzzy Decision Systems.

IEEE transactions on neural networks and learning systems·2026

Same author

Data-Driven Cation Engineering Guides Electrolyte Design for Sustainable Aqueous Zinc Battery Chemistries.

Advanced materials (Deerfield Beach, Fla.)·2026

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

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

Search research articles

Related Experiment Video

Updated: Feb 8, 2026

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

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

Published on: December 9, 2012

Multidirectional Prediction Approach for Dynamic Multiobjective Optimization Problems.

Miao Rong, Dunwei Gong, Yong Zhang

IEEE Transactions on Cybernetics

|July 12, 2018

Summary

This summary is machine-generated.

This study introduces a multidirectional prediction strategy to improve evolutionary algorithms (EAs) for dynamic multiobjective optimization problems (DMOPs). The novel approach enhances the ability of EAs to track moving Pareto fronts after environmental changes.

More Related Videos

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach

Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach

Published on: December 1, 2011

Related Experiment Videos

Last Updated: Feb 8, 2026

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

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

Published on: December 9, 2012

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach

Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach

Published on: December 1, 2011

Area of Science:

Computational Intelligence
Optimization Theory

Background:

Dynamic multiobjective optimization problems (DMOPs) require evolutionary algorithms (EAs) to track moving Pareto fronts.
Existing methods for predicting Pareto set (PS) movement have limitations.

Purpose of the Study:

To present a novel multidirectional prediction strategy to enhance EA performance in solving DMOPs.
To improve the accuracy of predicting the moving Pareto set (PS) location.

Main Methods:

A multidirectional prediction strategy is proposed for EAs.
A classification strategy is employed to cluster populations into representative groups.
The number of clusters adapts to the intensity of environmental changes.

Main Results:

The proposed prediction strategy was compared against four state-of-the-art methods.
Performance was evaluated using particle swarm optimization and five popular EAs for DMOPs.
Experimental results confirmed the effectiveness of the proposed algorithm.

Conclusions:

The developed multidirectional prediction strategy effectively tackles dynamic multiobjective optimization problems.
The adaptive clustering approach enhances the prediction accuracy of the moving Pareto set.