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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

88
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...
88
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.2K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.2K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

622
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
622
Probability Distributions01:32

Probability Distributions

7.7K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
7.7K
Binomial Probability Distribution01:15

Binomial Probability Distribution

11.2K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
11.2K
Randomized Experiments01:13

Randomized Experiments

7.1K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.1K

You might also read

Related Articles

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

Sort by
Same author

Quantitative molecular bioluminescence tomography.

Journal of biomedical optics·2022
Same author

Simultaneous diffuse optical and bioluminescence tomography to account for signal attenuation to improve source localization.

Biomedical optics express·2020
Same author

Single pixel hyperspectral bioluminescence tomography based on compressive sensing.

Biomedical optics express·2019
Same author

Multi-scale stochastic organization-oriented coarse-graining exemplified on the human mitotic checkpoint.

Scientific reports·2019
Same author

Landscape Analysis of a Class of NP-Hard Binary Packing Problems.

Evolutionary computation·2018
Same author

Organisation-Oriented Coarse Graining and Refinement of Stochastic Reaction Networks.

IEEE/ACM transactions on computational biology and bioinformatics·2018
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: Aug 8, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K

Evolutionary and Estimation of Distribution Algorithms for Unconstrained, Constrained, and Multiobjective Noisy

Aishwaryaprajna1, Jonathan E Rowe2,3

  • 1School of Computer Science, University of Birmingham, Birmingham, United Kingdom aishwaryaprajna@gmail.com.

Evolutionary Computation
|February 28, 2023
PubMed
Summary
This summary is machine-generated.

This study shows that the UMDA algorithm and its variants are effective for noisy combinatorial optimization problems. They outperform other evolutionary algorithms, especially under increasing noise levels.

Keywords:
NSGA-IINoisy combinatorial optimisationcrossoverestimation of distribution algorithmsexpected runtimenoisy multiobjective optimisation

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

11.7K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K

Related Experiment Videos

Last Updated: Aug 8, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
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

11.7K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K

Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Evolutionary computation

Background:

  • Combinatorial optimization problems are prevalent in various fields.
  • Handling noise in optimization is a significant challenge.
  • Existing evolutionary algorithms may struggle with noisy environments.

Purpose of the Study:

  • To empirically evaluate evolutionary algorithms on noisy combinatorial optimization problems.
  • To identify algorithms robust to noise.
  • To compare performance across different problem complexities and objectives.

Main Methods:

  • Testing algorithms including UMDA, PCEA, SEMO, and NSGA-II.
  • Experiments conducted on toy problems (OneMax), complex problems (SubsetSum, Knapsack, SetCover), and multiobjective problems.
  • Systematic analysis of performance under varying noise levels.

Main Results:

  • UMDA and PCEA demonstrated robustness to noise on toy problems.
  • UMDA outperformed PCEA on more complex noisy problems.
  • UMDA variants showed effectiveness on noisy multiobjective problems, outperforming SEMO and NSGA-II.

Conclusions:

  • UMDA and its variants are highly effective for noisy combinatorial optimization.
  • These algorithms offer a robust solution for problems with significant noise.
  • UMDA represents a superior choice compared to many other evolutionary algorithms in noisy settings.