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

Maximum Power Transfer01:16

Maximum Power Transfer

Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...

You might also read

Related Articles

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

Sort by
Same author

Optimizing Maritime Search and Rescue Planning via Genetic Algorithms: Incorporating Civilian Vessel Collaboration.

Biomimetics (Basel, Switzerland)·2025
Same author

Evolutionary Approach to Optimal Oil Skimmer Assignment for Oil Spill Response: A Case Study.

Biomimetics (Basel, Switzerland)·2024
Same author

Metaheuristic-Based Feature Selection Methods for Diagnosing Sarcopenia with Machine Learning Algorithms.

Biomimetics (Basel, Switzerland)·2024
Same author

Comparative Study of Classification Algorithms for Various DNA Microarray Data.

Genes·2022
Same author

Maximizing the Coverage of Sensor Deployments Using a Memetic Algorithm and Fast Coverage Estimation.

IEEE transactions on cybernetics·2021
Same author

Optimizing taxon addition order and branch lengths in the construction of phylogenetic trees using maximum likelihood.

Journal of bioinformatics and computational biology·2020
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: May 10, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.

Yourim Yoon, Yong-Hyuk Kim

    IEEE Transactions on Cybernetics
    |June 13, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an efficient genetic algorithm for optimizing wireless sensor network deployment, significantly improving coverage quality and speed compared to random methods. The novel approach enhances sensor network performance for applications like surveillance and monitoring.

    Related Experiment Videos

    Last Updated: May 10, 2026

    Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
    08:58

    Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

    Published on: October 17, 2025

    Area of Science:

    • Computer Science
    • Electrical Engineering
    • Network Engineering

    Background:

    • Sensor networks are crucial for applications like battlefield surveillance, environmental monitoring, and industrial diagnostics.
    • Network coverage is a key performance metric, indicating the effectiveness of sensor field monitoring.
    • Random deployment of sensor nodes can lead to inefficient and unbalanced coverage.

    Purpose of the Study:

    • To address the maximum coverage deployment problem in wireless sensor networks.
    • To propose an efficient genetic algorithm for optimizing sensor node placement.
    • To enhance deployment strategies beyond random methods for improved network performance.

    Main Methods:

    • Analysis of the maximum coverage deployment problem and its solution space.
    • Development of an efficient genetic algorithm leveraging a novel normalization method.
    • Utilization of a Monte Carlo method for an efficient evaluation function with adaptive sampling.
    • Incorporation of a local search mechanism to further refine the genetic algorithm.

    Main Results:

    • The proposed genetic algorithm demonstrates a significant improvement in coverage quality.
    • The algorithm achieves approximately twice the speed of existing methods and random deployment.
    • A comparative experimental study validates the performance enhancements.

    Conclusions:

    • The developed genetic algorithm offers a superior solution for maximum coverage deployment in wireless sensor networks.
    • The novel approach provides a faster and more effective method for sensor network deployment compared to traditional techniques.
    • Further improvements are possible by integrating well-designed local search strategies.