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

38
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...
38
Response Surface Methodology01:16

Response Surface Methodology

89
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
89
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.0K
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.3K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.3K
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

380
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...
380
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

95
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...
95

You might also read

Related Articles

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

Sort by
Same author

Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.

Frontiers in public health·2024
Same author

Machine learning models in predicting health care costs in patients with a recent acute coronary syndrome: A prospective pilot study.

Cardiovascular digital health journal·2023
Same author

Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems.

Evolutionary computation·2023
Same author

Interactive multiobjective optimization for finding the most preferred exercise therapy modality in knee osteoarthritis.

Annals of medicine·2022
Same author

Potential of interactive multiobjective optimization in supporting the design of a groundwater biodenitrification process.

Journal of environmental management·2019
Same author

Projections onto the Pareto surface in multicriteria radiation therapy optimization.

Medical physics·2015
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: Jun 1, 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

12.9K

Survey of interactive evolutionary decomposition-based multiobjective optimization methods.

Giomara Lárraga1, Kaisa Miettinen2

  • 1University of Jyvaskyla, Faculty of Information Technology, Finland giomara.g.larraga-maldonado@jyu.fi.

Evolutionary Computation
|January 17, 2025
PubMed
Summary
This summary is machine-generated.

This paper reviews interactive evolutionary decomposition-based multiobjective optimization methods. It identifies desirable properties for real-world application, aiming to reduce decision-maker burden and improve final solution selection.

Keywords:
Interactive methodsevolutionary methods.multiobjective optimization

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.0K

Related Experiment Videos

Last Updated: Jun 1, 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

12.9K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.0K

Area of Science:

  • Optimization
  • Evolutionary Computation
  • Decision Support Systems

Background:

  • Multiobjective optimization problems involve simultaneous optimization of multiple conflicting objectives.
  • Interactive methods aid decision-makers by incorporating preferences iteratively.
  • Decomposition-based methods are popular for many-objective problems, but their interactive versions often lack desirable real-world properties.

Purpose of the Study:

  • To review existing interactive evolutionary decomposition-based multiobjective optimization methods.
  • To analyze methodologies for incorporating interactivity into these methods.
  • To identify desirable properties for enhancing the practical applicability of these methods.

Main Methods:

  • Literature review of interactive evolutionary decomposition-based multiobjective optimization.
  • Analysis of different interactivity incorporation methodologies.
  • Identification and discussion of desirable properties for interactive methods.

Main Results:

  • Several interactive evolutionary decomposition-based methods exist, but many fall short of practical requirements.
  • Various techniques are used to integrate interactivity, with differing impacts on user experience.
  • Key desirable properties include reduced cognitive load, user control over interaction, and support for final solution selection.

Conclusions:

  • There is a need for interactive evolutionary decomposition-based methods that better align with real-world decision-making processes.
  • Future research should focus on developing methods that incorporate identified desirable properties.
  • Enhancing these methods will improve their usability and effectiveness in solving complex multiobjective optimization problems.