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

Evolutionary Psychology01:20

Evolutionary Psychology

703
Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
703
The Evidence for Evolution02:55

The Evidence for Evolution

46.8K
Genetic variations accumulating within populations over generations give rise to biological evolution. Evolutionary changes can result in the formation of novel varieties and entire new species. These changes are responsible for the diverse forms of life inhabiting the planet. The evidence for evolution suggests that all living organisms descended from common ancestors.
46.8K
Eukaryotic Evolution01:24

Eukaryotic Evolution

39.5K
The endosymbiont theory is the most widely accepted theory of eukaryotic evolution; however, its progression is still somewhat debated. According to the nucleus-first hypothesis, the ancestral prokaryote first evolved a membrane to enclose DNA and form the nucleus. Conversely, the mitochondria-first hypothesis suggests that the nucleus was formed after endosymbiosis of mitochondria.
Contrary to the endosymbiont theory, the eukaryote-first hypothesis proposes that the simpler prokaryotic and...
39.5K
Convergent Evolution01:54

Convergent Evolution

30.7K
Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.
30.7K
What is Evolutionary History?02:35

What is Evolutionary History?

42.2K
Scientists record evolutionary history by analyzing fossil, morphological, and genetic data. The fossil record documents the history of life on Earth and provides evidence for evolution. However, both fossil and living organisms offer evidence that outlines Earth’s evolutionary history.
42.2K
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

8.8K
While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
8.8K

You might also read

Related Articles

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

Sort by
Same author

Large language models help computer programs to evolve.

Nature·2024
Same author

Precise characterization of a corridor-shaped structure in Khufu's Pyramid by observation of cosmic-ray muons.

Nature communications·2023
Same author

Signal-Based Self-Organization of a Chain of UAVs for Subterranean Exploration.

Frontiers in robotics and AI·2021
Same author

Adaptive Prior Selection for Repertoire-Based Online Adaptation in Robotics.

Frontiers in robotics and AI·2021
Same author

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities.

Artificial life·2020
Same author

Data-Efficient Design Exploration through Surrogate-Assisted Illumination.

Evolutionary computation·2018
Same journal

Advancing microalgae biomass cultivation for an integrated sustainable wastewater treatment and resource recovery.

iScience·2026
Same journal

Corrigendum to "Human adipose ECM alleviates radiation-induced skin fibrosis via endothelial cell-mediated M2 macrophage polarization" [iScience, Volume 26, Issue 9 (2023) 107660].

iScience·2026
Same journal

High-definition transcranial direct current stimulation enhances exercise-induced hypoalgesia in patients with chronic low back pain.

iScience·2026
Same journal

From pre-tumor to tumor: Decoding the endoscopic-pathologic spectrum of neoplastic lesions in autoimmune gastritis.

iScience·2026
Same journal

Corrigendum to "A cobalt-aluminium layered double hydroxide with a nickel core-shell structure nanocomposite for supercapacitor applications" [iScience, 28 (2025) 111672].

iScience·2026
Same journal

Repurposing primaquine diphosphate for imatinib-resistant chronic myeloid leukemia via targeting BCR-ABL and Wnt/β-catenin pathway.

iScience·2026
See all related articles

Related Experiment Video

Updated: Nov 29, 2025

Author Spotlight: Advancing Protein Engineering – Harnessing Evolution Through PRANCE and Lab Automation
05:08

Author Spotlight: Advancing Protein Engineering – Harnessing Evolution Through PRANCE and Lab Automation

Published on: January 12, 2024

2.0K

Evolving the Behavior of Machines: From Micro to Macroevolution.

Jean-Baptiste Mouret1

  • 1Inria, CNRS, Université de Lorraine, LORIA, Nancy 54000, France.

Iscience
|November 23, 2020
PubMed
Summary
This summary is machine-generated.

Neuroevolution, inspired by natural evolution, now focuses on creating diverse species (macroevolution) rather than just optimizing single solutions. This approach fosters creativity and opens new applications in machine learning and design.

Keywords:
Computer ScienceEvolutionary MechanismsSoftware Evolution

More Related Videos

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
15:00

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

Published on: August 18, 2023

4.0K
Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER
07:26

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

Published on: May 19, 2019

12.4K

Related Experiment Videos

Last Updated: Nov 29, 2025

Author Spotlight: Advancing Protein Engineering – Harnessing Evolution Through PRANCE and Lab Automation
05:08

Author Spotlight: Advancing Protein Engineering – Harnessing Evolution Through PRANCE and Lab Automation

Published on: January 12, 2024

2.0K
Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
15:00

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

Published on: August 18, 2023

4.0K
Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER
07:26

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

Published on: May 19, 2019

12.4K

Area of Science:

  • Artificial Intelligence
  • Evolutionary Computation

Background:

  • Natural evolution produces highly sophisticated systems.
  • Neuroevolution, a computational approach, has historically focused on optimization.
  • Recent trends emphasize evolutionary creativity and diversity.

Purpose of the Study:

  • To shift the focus of artificial evolution from microevolution to macroevolution.
  • To explore the creative potential of evolutionary processes.
  • To leverage evolutionary principles for generating diverse solutions.

Main Methods:

  • Utilizing evolutionary algorithms to evolve complex systems.
  • Moving beyond simple fitness-based optimization.
  • Exploring methods that promote species diversity.

Main Results:

  • Demonstrated a shift from optimizing single solutions to generating diverse species.
  • Opened new avenues for artificial evolution beyond mere optimization.
  • Showcased applications in evolving diverse gaits, game content, and designs.

Conclusions:

  • Modern artificial evolution emphasizes macroevolution and diversity.
  • This approach unlocks greater creative potential in computational systems.
  • Neuroevolution is expanding into novel application domains.