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

State Function, Exact and Inexact Differentials01:27

State Function, Exact and Inexact Differentials

A state function is a thermodynamic property that depends solely on the current state of a system, irrespective of its history or how it arrived at that state. These functions are represented by capital letters, such as U, H, and S, which stand for internal energy, enthalpy, and entropy, respectively.For instance, the value of internal energy depends on the system's state variables and remains unaffected by the process path. This means that whether the system underwent a linear process or a...
Separable Differential Equations01:20

Separable Differential Equations

A separable differential equation is a type of first-order differential equation where the derivative dy/dx can be expressed as a product of two functions: one that depends only on x and another that depends only on y. This allows for the rearrangement of the equation so that all terms involving y are on one side, and all terms involving x are on the other. This process, known as the separation of variables, simplifies the process of solving the equation by enabling the integration of both...
Geometric Sequences01:30

Geometric Sequences

In systems where values diminish by a constant proportion at each stage, the resulting sequence follows a geometric structure. Each new value in the sequence is obtained by applying a fixed multiplier to the preceding term. This regular, proportional decline type is often used to represent processes involving gradual loss, such as energy dissipation or reduction in amplitude over time.When analyzing the total effect of such a process across unlimited iterations, the series of values is referred...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Coordination Number and Geometry02:57

Coordination Number and Geometry

For transition metal complexes, the coordination number determines the geometry around the central metal ion. Table 1 compares coordination numbers to molecular geometry. The most common structures of the complexes in coordination compounds are octahedral, tetrahedral, and square planar.
Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured from the...

You might also read

Related Articles

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

Sort by
Same author

Simulation-based training in ultrasound-guided pediatric central venous catheterization for anesthesiology residents: transfer to the clinical setting.

Advances in simulation (London, England)·2026
Same author

Statin use and low-density lipoprotein cholesterol target achievement for primary prevention of atherosclerotic cardiovascular disease in patients with type 2 diabetes mellitus: a multicenter cross-sectional study in Sri Lanka.

PloS one·2025
Same author

Ultrasonographic Assessment of Testicular Biometry and Arterial Blood Flow in Pre- and Postpubertal Miranda Donkeys: Correlations With Semen Quality Parameters.

Reproduction in domestic animals = Zuchthygiene·2024
Same author

Pseudo-Meigs syndrome due to bilateral serous ovarian adenocarcinoma: A case report.

SAGE open medical case reports·2024
Same author

A review of computational methodologies to predict the fractional flow reserve in coronary arteries with stenosis.

Journal of biomechanics·2024
Same author

Antimicrobial resistance patterns of Staphylococcus spp. isolated from clinical specimens of companion animals in Northern Portugal, 2021-2023.

Veterinary journal (London, England : 1997)·2024
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: May 15, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Geometric differential evolution for combinatorial and programs spaces.

A Moraglio1, J Togelius, S Silva

  • 1School of Computer Science, University of Birmingham, UK A.Moraglio@cs.bham.ac.uk.

Evolutionary Computation
|December 29, 2012
PubMed
Summary
This summary is machine-generated.

Geometric differential evolution (GDE) offers a unified framework for creating specialized algorithms for continuous and combinatorial optimization. New GDE algorithms derived for binary strings, permutations, and genetic programs show competitive performance against established search methods.

More Related Videos

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Related Experiment Videos

Last Updated: May 15, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Area of Science:

  • Computational intelligence
  • Evolutionary computation
  • Optimization algorithms

Background:

  • Traditional differential evolution (DE) lacks a unified framework for diverse search spaces.
  • Geometric differential evolution (GDE) provides a formal generalization of DE.
  • GDE retains a consistent geometric interpretation of search dynamics across different representations.

Purpose of the Study:

  • To review the theory behind the GDE algorithm.
  • To formally derive GDE algorithms for specific search spaces: binary strings, permutations, vectors of permutations, and genetic programs.
  • To evaluate the performance of these novel GDE algorithms.

Main Methods:

  • Theoretical review of Geometric Differential Evolution (GDE).
  • Formal derivation of representation-specific GDE algorithms.
  • Experimental evaluation on benchmark problems including NK-landscapes, Traveling Salesperson Problem (TSP), Sudoku, and genetic programming tasks (regression, artificial ant, parity, multiplexer).

Main Results:

  • Successfully derived and implemented GDE algorithms tailored for binary strings, permutations, vectors of permutations, and genetic programs.
  • Experimental results demonstrate that the new GDE algorithms are competitive with well-tuned standard search algorithms across various problem domains.
  • The GDE framework effectively bridges continuous and combinatorial optimization through a unified geometric interpretation.

Conclusions:

  • The GDE framework provides a powerful and flexible approach for developing specialized evolutionary algorithms.
  • Representation-specific GDE algorithms offer competitive performance, validating the theoretical framework.
  • This work extends the applicability of differential evolution to a wider range of complex optimization problems.