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

Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

6.2K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
6.2K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

2.5K
2.5K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.9K
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...
5.9K
Limits to Natural Selection01:38

Limits to Natural Selection

30.0K
Organisms that are well-adapted to their environment are more likely to survive and reproduce. However, natural selection does not lead to perfectly adapted organisms. Several factors constrain natural selection.
30.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

441
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
441
Genetic Drift03:33

Genetic Drift

35.4K
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.
35.4K

You might also read

Related Articles

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

Sort by
Same author

The Cost of Randomness in Evolutionary Algorithms: Crossover Can Save Random Bits.

Evolutionary computation·2025
Same author

Runtime Analysis of Restricted Tournament Selection for Bimodal Optimisation.

Evolutionary computation·2021
Same author

On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation.

Algorithmica·2020
Same author

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

Algorithmica·2019
Same author

On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism.

Evolutionary computation·2018
Same author

Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem.

Evolutionary computation·2016
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 6, 2026

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

5.1K

General upper bounds on the runtime of parallel evolutionary algorithms.

Jörg Lässig1, Dirk Sudholt

  • 1Department of Computer Science, University of Applied Sciences Zittau/Görlitz, Germany jlaessig@hszg.de.

Evolutionary Computation
|November 8, 2013
PubMed
Summary
This summary is machine-generated.

We developed a method to analyze parallel evolutionary algorithm runtime, estimating speedup from parallelization. This technique works for various population structures and migration topologies, offering practical insights for algorithm parameterization.

Keywords:
Parallel evolutionary algorithmsisland modelruntime analysisspatial structures

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.2K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.2K

Related Experiment Videos

Last Updated: May 6, 2026

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

5.1K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.2K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.2K

Area of Science:

  • Computational intelligence
  • Algorithm analysis
  • Parallel computing

Background:

  • Analyzing the runtime of parallel evolutionary algorithms (PEAs) with structured populations is complex.
  • Estimating the speedup gained from parallelization requires rigorous methods.

Purpose of the Study:

  • To present a general method for analyzing the runtime of PEAs with spatially structured populations.
  • To provide rigorous estimates of speedup gained by parallelization.
  • To offer guidance on parameterizing PEAs.

Main Methods:

  • The fitness-level method is adapted to yield upper bounds on expected parallel runtime.
  • Analysis is tailored for common migration topologies including ring graphs, torus graphs, hypercubes, and complete graphs.

Main Results:

  • The method is easy to apply and provides powerful results for pseudo-Boolean optimization.
  • Performance guarantees improve with topology density.
  • Sparse topologies like ring graphs can yield significant speedup without substantially increasing function evaluations.

Conclusions:

  • The developed method offers a powerful tool for analyzing PEA performance.
  • Even sparse topologies can provide substantial speedups, challenging previous assumptions.
  • The study identifies optimal processor counts for guaranteed speedups, aiding PEA design.