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

Point and Frameshift Mutations01:30

Point and Frameshift Mutations

38
Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
38
Mismatch Repair01:20

Mismatch Repair

4.9K
Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...
4.9K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

58.7K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
58.7K
Mutations01:39

Mutations

83.8K
Overview
83.8K
Survival Tree01:19

Survival Tree

119
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
119
Mutations in Microorganisms01:18

Mutations in Microorganisms

36
Mutations are heritable changes in an organism’s genome involving alterations in the base sequence of DNA or RNA. These changes can influence cellular processes and phenotypic traits, potentially transforming the unaltered wild type into a mutant form. Such changes, termed forward mutations, are pivotal in shaping the genetic diversity of organisms.RNA viruses exhibit the highest mutation rates due to the absence of robust proofreading mechanisms during genome replication. In contrast,...
36

You might also read

Related Articles

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

Sort by
Same author

MO-SMAC: Multiobjective Sequential Model-Based Algorithm Configuration.

Evolutionary computation·2025
Same author

Social Bots: Human-Like by Means of Human Control?

Big data·2017
Same author

Leveraging TSP Solver Complementarity through Machine Learning.

Evolutionary computation·2017
Same author

Competitive coevolutionary learning of fuzzy systems for job exchange in computational grids.

Evolutionary computation·2009
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: Jul 27, 2025

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

13.0K

On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem.

Jakob Bossek1, Christian Grimme2

  • 1AI Methodology, Department of Computer Science, RWTH Aachen University, Germany bossek@aim.rwth-aachen.de.

Evolutionary Computation
|June 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces novel evolutionary computation operators for the multiobjective minimum spanning tree problem (moMST). These efficient sub-graph-based mutation operators outperform existing methods, even with limited computational resources.

Keywords:
Evolutionary algorithmsbiased mutationcombinatorial optimizationminimum spanning tree problemmultiobjective optimization

More Related Videos

Identifying DNA Mutations in Purified Hematopoietic Stem/Progenitor Cells
11:06

Identifying DNA Mutations in Purified Hematopoietic Stem/Progenitor Cells

Published on: February 24, 2014

13.1K
Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria
08:33

Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria

Published on: July 28, 2023

647

Related Experiment Videos

Last Updated: Jul 27, 2025

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

13.0K
Identifying DNA Mutations in Purified Hematopoietic Stem/Progenitor Cells
11:06

Identifying DNA Mutations in Purified Hematopoietic Stem/Progenitor Cells

Published on: February 24, 2014

13.1K
Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria
08:33

Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria

Published on: July 28, 2023

647

Area of Science:

  • Computer Science
  • Operations Research
  • Artificial Intelligence

Background:

  • The multiobjective minimum spanning tree problem (moMST) is a computationally challenging NP-hard problem.
  • Approximating the Pareto-set for moMST is crucial for understanding trade-offs in multi-objective optimization.

Purpose of the Study:

  • To develop efficient evolutionary computation operators for approximating the Pareto-set of the moMST problem.
  • To analyze the neighborhood structure of Pareto-optimal spanning trees to design effective mutation operators.

Main Methods:

  • Design and implementation of novel sub-graph-based mutation operators for evolutionary algorithms.
  • Analysis of runtime complexity and the Pareto-beneficial property of the proposed operators.
  • Extensive experimental benchmarking against established baseline algorithms.

Main Results:

  • The developed sub-graph-based operators demonstrate superior performance compared to baseline algorithms.
  • Effective approximation of the Pareto-set achieved even with a restricted computational budget.
  • Operators show practical suitability across diverse graph classes with varying Pareto-front shapes.

Conclusions:

  • The proposed evolutionary computation approach offers an efficient method for solving the moMST problem.
  • Sub-graph-based mutation operators provide a significant advancement in approximating Pareto-optimal solutions.
  • The study confirms the practical viability and efficiency of the new operators in multi-objective optimization.