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

Genetic Lingo01:11

Genetic Lingo

112.6K
Overview
112.6K
Combinatorial Gene Control02:33

Combinatorial Gene Control

9.2K
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...
9.2K
Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

868
In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
868
Language Development01:22

Language Development

686
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
686
Structure of a Gene01:30

Structure of a Gene

15.0K
A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
However, only 1% of the DNA is composed of genes that encode proteins; the rest, 99% is non-coding DNA. This non-coding DNA performs...
15.0K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

810
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
810

You might also read

Related Articles

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

Sort by
Same author

Translation of scRNA-seq to a clinical blood test for infection diagnostics.

Expert review of molecular diagnostics·2026
Same author

Direct single cell-type gene expression analysis in peripheral blood: novel ratio-based gene expression biomarkers using 2 novel monocyte reference genes (PSAP and CTSS) for detection of bacterial infection.

Human molecular genetics·2025
Same author

scCaT: An explainable capsulating architecture for sepsis diagnosis transferring from single-cell RNA sequencing.

PLoS computational biology·2024
Same author

Detection of Suicidal Ideation in Clinical Interviews for Depression Using Natural Language Processing and Machine Learning: Cross-Sectional Study.

JMIR medical informatics·2023
Same author

bvnGPS: a generalizable diagnostic model for acute bacterial and viral infection using integrative host transcriptomics and pretrained neural networks.

Bioinformatics (Oxford, England)·2023
Same author

Whole transcriptome analysis reveals non-coding RNA's competing endogenous gene pairs as novel form of motifs in serous ovarian cancer.

Computers in biology and medicine·2022
Same journal

Computing Optimal Populations for Binary Problems using Logic Minimization.

Evolutionary computation·2026
Same journal

Enhancing Generalization and Scalability for Multi-Objective Optimization with Population Pre-Training.

Evolutionary computation·2026
Same journal

XCS for Sequential Perceptual Aliasing in Multi-Step Decision Making.

Evolutionary computation·2026
Same journal

A dynamic multi-objective evolutionary algorithm using dual-space prediction and surrogate-based sampling.

Evolutionary computation·2026
Same journal

Adapting MOEA/D to CMA-ES for Dealing with Ill-conditioned Multiobjective Problems.

Evolutionary computation·2026
Same journal

Editorial of the Special Issue: Parallel Problem Solving from Nature PPSN 2024 Extended Versions of Best Paper Candidates.

Evolutionary computation·2026
See all related articles

Related Experiment Video

Updated: Dec 6, 2025

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
05:33

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

6.3K

Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming.

Pak-Kan Wong1, Man-Leung Wong2, Kwong-Sak Leung3

  • 1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong pkwong@link.cuhk.edu.hk.

Evolutionary Computation
|October 13, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces Grammar-Based Genetic Programming with Bayesian Classifiers (GBGPBC) to improve evolutionary program creation. GBGPBC enhances robustness and efficiency in finding optimal programs, outperforming other Genetic Programming methods.

Keywords:
Bayesian network classifierEstimation of distribution programmingadaptive grammar-based genetic programmingdata mining.

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.5K
Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
05:22

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies

Published on: May 9, 2019

5.6K

Related Experiment Videos

Last Updated: Dec 6, 2025

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
05:33

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

6.3K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.5K
Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
05:22

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies

Published on: May 9, 2019

5.6K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Evolutionary Computation

Background:

  • Genetic Programming (GP) faces challenges with program deceptiveness due to complex component dependencies, leading to suboptimal solutions.
  • Minor program modifications can cause significant behavioral changes and affect final outputs, complicating the search for effective programs.

Purpose of the Study:

  • To present Grammar-Based Genetic Programming with Bayesian Classifiers (GBGPBC) for capturing probabilistic dependencies among program components.
  • To enhance the robustness and efficiency of Genetic Programming search for optimal programs.

Main Methods:

  • Utilized Bayesian network classifiers to model probabilistic dependencies within program structures.
  • Evaluated the GBGPBC system on benchmark problems including deceptive maximum, royal tree, and bipolar asymmetric royal tree problems.
  • Investigated factors influencing GBGPBC performance and its correlation with complexity measures.

Main Results:

  • GBGPBC demonstrated superior robustness and efficiency in program search compared to other Genetic Programming approaches, measured by fitness evaluations.
  • Robust GBGPBC variants showed a weak correlation with certain complexity measures.
  • Applied GBGPBC to a direct marketing customer ranking task, yielding significantly higher earnings than traditional machine learning algorithms.

Conclusions:

  • GBGPBC effectively addresses deceptiveness in Genetic Programming by incorporating Bayesian network classifiers.
  • The proposed method offers a more robust and efficient approach to evolutionary program synthesis.
  • GBGPBC shows practical utility in real-world applications like direct marketing, outperforming established machine learning techniques.