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

Escape Velocity01:26

Escape Velocity

8.3K
The escape velocity of an object is defined as the minimum initial velocity that it requires to escape the surface of another object to which it is gravitationally bound and never to return. For example, what would be the minimum velocity at which a satellite should be launched from the Earth's surface such that it just escapes the Earth's gravitational field?
To calculate the escape velocity, it is assumed that no energy is lost to any frictional forces. In practice, a satellite...
8.3K
Escape Velocities of Gases01:19

Escape Velocities of Gases

1.4K
To escape the Earth's gravity, an object near the top of the atmosphere at an altitude of 100 km must travel away from Earth at 11.1 km/s. This speed is called the escape velocity. The temperature at which gas molecules attain the rms speed, which is equal to the escape velocity, can be estimated by using the equation for the average kinetic energy of the gas molecules. According to the kinetic theory of gas, the average kinetic energy of the gas molecules is proportional to its...
1.4K
Detection of Black Holes01:10

Detection of Black Holes

2.5K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.5K
Microsoft Excel: Median, Quartile range, and Box Plots01:29

Microsoft Excel: Median, Quartile range, and Box Plots

3.2K
In Microsoft Excel, calculating the median, interquartile range, and creating box plots can help understand the distribution of your data.
Median and Quartile Range: The median is calculated using the formula `=MEDIAN(range)', which provides the middle value of your data set. Quartiles divide your data into four equal parts. To find the first and third quartiles, use ‘=QUARTILE(range, 1)' and ‘=QUARTILE(range, 3)', respectively. The interquartile range (IQR), which...
3.2K
Local Attraction01:22

Local Attraction

370
Local attraction refers to disturbances in compass readings caused by magnetic influences from nearby objects such as metal fences, buried pipes, vehicles, buildings, power lines, or natural iron ore deposits. Small items like wristwatches, steel tools, or belt buckles can also interfere with the compass by creating local magnetic fields that distort the Earth's natural magnetic field. These distortions lead to inaccurate readings, posing navigation and land surveying challenges.Local...
370
Local Anesthetics: Pharmacokinetics01:13

Local Anesthetics: Pharmacokinetics

1.2K
The potency and duration of action of local anesthetics (LAs) are determined by their pharmacokinetics. Pharmacokinetics describes how LAs are absorbed, distributed, metabolized, and eliminated from the body. When administered to the vascular tissues, LAs are quickly absorbed and enter the systemic circulation, reducing their localized effects. Adding vasoconstrictors such as epinephrine to LAs reduces their absorption into the systemic circulation, making them clinically effective. The...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Repression of ABA-responsive alternative splicing by an Arabidopsis SR protein relieves ABA inhibition of early plant growth.

The Plant cell·2026
Same author

Evolution of Ivermectin Resistance in the Nematode Model <i>Caenorhabditis elegans</i>: Critical Influence of Population Size and Altered Emodepside Efficacy.

Evolutionary applications·2026
Same author

Systematic review of triploidy among parasitic worms.

Parasitology·2026
Same author

Investigating the consequences of the mating system for drug resistance evolution in <i>Caenorhabditis elegans</i>.

Proceedings. Biological sciences·2025
Same author

A metabolite-based resistance mechanism against malaria.

Science (New York, N.Y.)·2025
Same author

The mutant selection window of moxifloxacin and bedaquiline resistant Mycobacterium tuberculosis.

The Journal of infection·2025
Same journal

Tree-Packing Revisited: Faster Fully Dynamic Min-Cut and Arboricity.

Algorithmica·2026
Same journal

A General Upper Bound for the Runtime of a Coevolutionary Algorithm on Impartial Combinatorial Games.

Algorithmica·2026
Same journal

Fully Characterizing Lossy Catalytic Computation.

Algorithmica·2026
Same journal

Parameterized Complexities of Dominating and Independent Set Reconfiguration.

Algorithmica·2026
Same journal

The SLO Hierarchy of Pseudo-Boolean Functions and Runtime of Evolutionary Algorithms.

Algorithmica·2026
Same journal

From Data Completion to Problems on Hypercubes: A Parameterized Analysis of the Independent Set Problem.

Algorithmica·2025
See all related articles

Related Experiment Video

Updated: Jan 26, 2026

Simultaneous Distinction of Monospecific and Mixed DFS70 Patterns During ANA Screening with a Novel HEp-2 ELITE/DFS70 Knockout Substrate
10:05

Simultaneous Distinction of Monospecific and Mixed DFS70 Patterns During ANA Screening with a Novel HEp-2 ELITE/DFS70 Knockout Substrate

Published on: January 17, 2018

37.2K

How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism.

Pietro S Oliveto1, Tiago Paixão2, Jorge Pérez Heredia1

  • 11University of Sheffield, Sheffield, S1 4DP UK.

Algorithmica
|April 19, 2019
PubMed
Summary
This summary is machine-generated.

Evolutionary algorithms (EA) struggle with fitness valleys, while Metropolis and Strong Selection Weak Mutation (SSWM) algorithms efficiently navigate them. Valley depth critically impacts non-elitist algorithm performance, unlike EA

Keywords:
Black box optimisationEvolutionary algorithmsMetropolis algorithmPopulation geneticsRuntime analysisSimulated annealingStrong selection weak mutation regime

More Related Videos

Optogenetic Stimulation of Escape Behavior in Drosophila melanogaster
08:03

Optogenetic Stimulation of Escape Behavior in Drosophila melanogaster

Published on: January 25, 2013

18.0K
A Treatment Package without Escape Extinction to Address Food Selectivity
04:23

A Treatment Package without Escape Extinction to Address Food Selectivity

Published on: August 21, 2015

12.0K

Related Experiment Videos

Last Updated: Jan 26, 2026

Simultaneous Distinction of Monospecific and Mixed DFS70 Patterns During ANA Screening with a Novel HEp-2 ELITE/DFS70 Knockout Substrate
10:05

Simultaneous Distinction of Monospecific and Mixed DFS70 Patterns During ANA Screening with a Novel HEp-2 ELITE/DFS70 Knockout Substrate

Published on: January 17, 2018

37.2K
Optogenetic Stimulation of Escape Behavior in Drosophila melanogaster
08:03

Optogenetic Stimulation of Escape Behavior in Drosophila melanogaster

Published on: January 25, 2013

18.0K
A Treatment Package without Escape Extinction to Address Food Selectivity
04:23

A Treatment Package without Escape Extinction to Address Food Selectivity

Published on: August 21, 2015

12.0K

Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Population genetics

Background:

  • Escaping local optima is a key challenge in function optimization.
  • Fitness landscapes use hills and valleys to represent optimization problems.
  • Valley difficulty is defined by length (Hamming distance) and depth (fitness drop).

Purpose of the Study:

  • Compare runtimes of different stochastic search algorithms on tunable fitness valleys.
  • Analyze how valley characteristics affect algorithm performance.
  • Evaluate algorithm efficiency on rugged functions with consecutive valleys.

Main Methods:

  • Define a class of fitness valleys with tunable length and depth.
  • Compare runtime performance of (λ) Evolutionary Algorithm (EA), Metropolis algorithm, and Strong Selection Weak Mutation (SSWM) algorithm.
  • Test algorithms on a rugged function composed of sequential valleys.

Main Results:

  • The (λ) EA's runtime is critically dependent on valley length.
  • Non-elitist algorithms (Metropolis, SSWM) show runtime dependence on valley depth.
  • Both SSWM and Metropolis algorithms efficiently optimize rugged functions with consecutive valleys.

Conclusions:

  • Valley length is a critical factor for elitist algorithms like (λ) EA.
  • Valley depth is crucial for non-elitist algorithms, enabling efficient optimization.
  • Metropolis and SSWM algorithms demonstrate robustness in optimizing complex, rugged landscapes.