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

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Genetic Drift

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.

Genetic Variation

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

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).

Evolution of New Traits in Microbes

Evolution of New Traits in Microbes

Microorganisms evolve rapidly due to their large population sizes and short generation times, often exhibiting measurable changes within days under laboratory conditions. Natural selection acts on standing genetic variation, enabling the retention and amplification of beneficial traits that confer fitness advantages in changing environments.Adaptive Pigment Regulation in RhodobacterIn Rhodobacter, a genus of purple non-sulfur bacteria, light-harvesting pigments such as bacteriochlorophyll and...

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

You might also read

Related Articles

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

Sort by

Same author

Machine Learning Aided Kinematic Profiling of Reaching Movements Separates Spinocerebellar Ataxia type 12 and Essential Tremor.

Cerebellum (London, England)·2026

Same author

Leveraging Feature Alignment in Grassmannian Manifold for Multi-Output Regression Tasks.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Light regulation of seed-to-seedling transition under dynamic environment.

Plant physiology·2026

Same author

Chalcogenated biazulenediimides: A promising class of triplet harvesters.

The Journal of chemical physics·2025

Same author

Neural models and algorithms for sensorimotor control of an octopus arm.

Biological cybernetics·2025

Same author

Generative adversarial network augmented data for improved heart sound abnormality detection.

Computers in biology and medicine·2025

Search research articles

Related Experiment Video

Updated: May 17, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

A cluster-based differential evolution algorithm with external archive for optimization in dynamic environments.

Udit Halder¹, Swagatam Das, Dipnakar Maity

¹Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700 032, India. udithalder99@gmail.com

IEEE Transactions on Cybernetics

|October 26, 2012

Summary

A novel Cluster-based Dynamic Differential Evolution with external Archive (CDDE_Ar) algorithm optimizes dynamic fitness landscapes. This multipopulation approach adaptively clusters solutions, outperforming existing dynamic optimizers on benchmark problems.

More Related Videos

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

Published on: September 20, 2016

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Published on: August 18, 2023

Related Experiment Videos

Last Updated: May 17, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

Published on: September 20, 2016

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Published on: August 18, 2023

Area of Science:

Evolutionary Computation
Global Optimization
Artificial Intelligence

Background:

Dynamic fitness landscapes present significant challenges for traditional optimization algorithms.
Effective global optimization in changing environments requires adaptive strategies.

Purpose of the Study:

To introduce and evaluate a novel algorithm, Cluster-based Dynamic Differential Evolution with external Archive (CDDE_Ar), for global optimization in dynamic environments.
To assess the performance of CDDE_Ar against state-of-the-art dynamic optimizers.

Main Methods:

The CDDE_Ar algorithm employs a multipopulation strategy, partitioning solutions into adaptive clusters.
Clusters are evolved independently using differential evolution, with periodic population redistribution.
Information sharing occurs periodically through cluster adaptation and redistribution.

Main Results:

CDDE_Ar demonstrated statistically superior performance across a wide range of dynamic optimization problems (DOPs).
Comparative analysis was conducted using moving peaks benchmark problems and generalized-dynamic-benchmark-generator system.
The algorithm's effectiveness was validated against six leading dynamic optimizers.

Conclusions:

CDDE_Ar offers a robust and effective approach for global optimization in dynamic fitness landscapes.
The adaptive clustering and information sharing mechanisms contribute to its superior performance.
The proposed method represents a significant advancement in dynamic evolutionary optimization.