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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

139
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
139
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

3.1K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
3.1K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

133
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
133
Evolutionary Psychology01:20

Evolutionary Psychology

369
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...
369
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

543
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
543
The Evidence for Evolution02:55

The Evidence for Evolution

43.3K
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.
43.3K

You might also read

Related Articles

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

Sort by
Same author

Sensory-motor control with large language models via iterative policy refinement.

Scientific reports·2026
Same author

Human Expertise and Large Language Model Embeddings in the Content Validity Assessment of Personality Tests.

Educational and psychological measurement·2025
Same author

Global progress in competitive co-evolution: a systematic comparison of alternative methods.

Frontiers in robotics and AI·2025
Same author

Transformers deep learning models for missing data imputation: an application of the ReMasker model on a psychometric scale.

Frontiers in psychology·2025
Same author

Exploring the Potential of Variational Autoencoders for Modeling Nonlinear Relationships in Psychological Data.

Behavioral sciences (Basel, Switzerland)·2024
Same author

Interaction Rules Supporting Effective Flocking Behavior.

Artificial life·2024
Same journal

Passive wheels on legged robots: a survey.

Frontiers in robotics and AI·2026
Same journal

Politeness cannot make up for robots' errors.

Frontiers in robotics and AI·2026
Same journal

Workers expect basic social skills but limited autonomy from future robots - a qualitative interview study and taxonomy for robot social skills.

Frontiers in robotics and AI·2026
Same journal

Human-robot interaction in sustainable hospitality: how robot type shapes customer emotions, green perceptions, and service loyalty.

Frontiers in robotics and AI·2026
Same journal

Dynamic variance-aware federated tuning for efficient autonomous vehicle perception under non-IID settings.

Frontiers in robotics and AI·2026
Same journal

MPM-based simulation and bounded-error compression of material points for magnetic tactile sensors.

Frontiers in robotics and AI·2026
See all related articles

Related Experiment Video

Updated: Aug 24, 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

1.7K

Phenotypic complexity and evolvability in evolving robots.

Nicola Milano1, Stefano Nolfi1

  • 1Institute of Cognitive Sciences and Technologies, CNR, Rome, Italy.

Frontiers in Robotics and AI
|October 21, 2022
PubMed
Summary
This summary is machine-generated.

Evolutionary algorithms often create compact solutions that are efficient but may lack evolvability. This study finds phenotypic complexity correlates with evolvability in soft robots, suggesting ways to improve evolution.

Keywords:
complexityelastic soft-robotsevolutionary roboticsevolvabilityevolving morphologies

More Related Videos

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.8K
Folding and Characterization of a Bio-responsive Robot from DNA Origami
07:59

Folding and Characterization of a Bio-responsive Robot from DNA Origami

Published on: December 3, 2015

14.7K

Related Experiment Videos

Last Updated: Aug 24, 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

1.7K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.8K
Folding and Characterization of a Bio-responsive Robot from DNA Origami
07:59

Folding and Characterization of a Bio-responsive Robot from DNA Origami

Published on: December 3, 2015

14.7K

Area of Science:

  • Evolutionary computation
  • Robotics
  • Developmental biology

Background:

  • Evolutionary algorithms (EAs) can produce compact solutions, offering benefits like efficiency.
  • However, these compact solutions may exhibit reduced evolvability, limiting future improvements.
  • The trade-off between solution compactness and evolvability is a key challenge in EA design.

Purpose of the Study:

  • To investigate the relationship between phenotypic complexity and evolvability in soft robots.
  • To understand why evolutionary algorithms favor compact solutions.
  • To identify strategies for enhancing the evolutionary process in artificial systems.

Main Methods:

  • Simulated evolution of soft robots with varying morphologies.
  • Analysis of the correlation between phenotypic complexity and evolvability metrics.
  • Comparative studies of different mutation rate strategies.

Main Results:

  • A significant positive correlation was observed between phenotypic complexity and evolvability in soft robots.
  • Phenotypically simple robots, while robust, demonstrated limited evolvability.
  • Selection pressure favors simplicity, which can hinder long-term adaptation.
  • Increasing mutation rates or favoring complexifying mutations improved evolutionary efficacy.

Conclusions:

  • Phenotypic complexity is a crucial factor influencing evolvability in evolutionary robotics.
  • The tendency towards compact solutions in EAs can be a double-edged sword, impacting adaptive potential.
  • Strategies that promote phenotypic complexity can enhance the efficiency and success of evolutionary processes.