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

Hybrid Zones02:29

Hybrid Zones

16.8K
Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.
16.8K
Frequency-dependent Selection01:21

Frequency-dependent Selection

21.8K
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.
21.8K
Genetics of Speciation02:16

Genetics of Speciation

19.0K
Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.
19.0K
Types of Selection01:46

Types of Selection

40.1K
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...
40.1K

You might also read

Related Articles

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

Sort by
Same author

Research Progress in Field Grading Materials for New Power Systems.

Molecules (Basel, Switzerland)·2026
Same author

Strigolactone-mediated architecture regulation and stress resilience: Insights and innovations for crop breeding.

Journal of integrative plant biology·2026
Same author

Genome-edited rice variety with low-cadmium accumulation in the grain.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Chloroplast sunscreening by protein condensates confers high-light tolerance.

Cell·2026
Same author

Nonlinear Electrical Conductivity and Thermal Conductivity of g-C<sub>3</sub>N<sub>4</sub>/Liquid Silicone Rubber Field Grading Composites.

Materials (Basel, Switzerland)·2026
Same author

Different direction adversarial sample for diffusion model.

Neural networks : the official journal of the International Neural Network Society·2026

Related Experiment Video

Updated: Jun 2, 2025

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

7.4K

An effective feature selection approach based on hybrid Grey Wolf Optimizer and Genetic Algorithm for hyperspectral

Yiqun Shang1,2, Minrui Zheng3, Jiayang Li4

  • 1School of Information Engineering, China University of Geosciences, Beijing, 100083, China.

Scientific Reports
|January 14, 2025
PubMed
Summary

A new hybrid algorithm, Grey Wolf Optimizer and Genetic Algorithm (GWOGA), improves hyperspectral image classification by balancing exploration and exploitation for effective feature selection.

More Related Videos

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

12.9K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

969

Related Experiment Videos

Last Updated: Jun 2, 2025

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

7.4K
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

12.9K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

969

Area of Science:

  • Remote Sensing
  • Computer Science
  • Artificial Intelligence

Background:

  • Feature selection (FS) is crucial for hyperspectral image (HSI) classification, reducing dimensionality while maintaining accuracy.
  • Existing swarm intelligence and evolutionary algorithms (SIEAs) struggle with exploration and local optima in high-dimensional HSIs.

Purpose of the Study:

  • To develop a novel hybrid algorithm, GWOGA, for effective feature selection in hyperspectral image classification.
  • To enhance the balance between exploration and exploitation in feature selection algorithms.

Main Methods:

  • Proposed GWOGA algorithm combining Grey Wolf Optimizer (GWO) and Genetic Algorithm (GA).
  • Incorporated chaotic map and Opposition-Based Learning (OBL) for population initialization to improve diversity.
  • Implemented an elite learning strategy and a hybrid optimization mechanism for efficient search and local optima avoidance.

Main Results:

  • GWOGA demonstrated superior performance compared to state-of-the-art algorithms on benchmark HSIs (Indian Pines, KSC, Botswana).
  • Achieved higher classification accuracy with a reduced number of selected bands.
  • Validated the robustness and generalizability of the proposed GWOGA algorithm.

Conclusions:

  • GWOGA offers an effective solution for feature selection in hyperspectral image classification.
  • The algorithm's hybrid approach successfully addresses limitations of existing SIEAs.
  • GWOGA shows significant potential for practical applications in HSI data analysis.