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

Combinatorial Gene Control02:33

Combinatorial Gene Control

9.7K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
9.7K
Synthesis and Decomposition Reactions02:17

Synthesis and Decomposition Reactions

38.4K
Synthesis and decomposition are two types of redox reactions. Synthesis means to make something, whereas decomposition means to break something. The reactions are accompanied by chemical and energy changes. 
38.4K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

430
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 of...
430
Multi-Step Reactions02:31

Multi-Step Reactions

8.9K
Chemical reactions often occur in a stepwise fashion involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs. Each of the steps in a reaction mechanism is called an elementary reaction. These...
8.9K
Rate-Determining Steps03:08

Rate-Determining Steps

37.5K
Relating Reaction Mechanisms
In a multistep reaction mechanism, one of the elementary steps progresses significantly slower than the others. This slowest step is called the rate-limiting step (or rate-determining step). A reaction cannot proceed faster than its slowest step, and hence, the rate-determining step limits the overall reaction rate.
The concept of rate-determining step can be understood from the analogy of a 4-lane freeway with a short-stretch of traffic-bottleneck caused due to...
37.5K
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

4.9K
Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
4.9K

You might also read

Related Articles

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

Sort by
Same author

Safe Fairness Guarantees Without Demographics in Classification: Spectral Uncertainty Set Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Multimodal fusion strategies for survival prediction in breast cancer: A comparative deep learning study.

Computational and structural biotechnology journal·2025
Same author

A network-based model to assess vaccination strategies for the COVID-19 pandemic by using Bayesian optimization.

Chaos, solitons, and fractals·2025
Same author

P-NP Instance Decomposition Based on the Fourier Transform for Solving the Linear Ordering Problem.

Evolutionary computation·2025
Same author

On the Use of the Doubly Stochastic Matrix Models for the Quadratic Assignment Problem.

Evolutionary computation·2025
Same author

A probabilistic generative model to discover the treatments of coexisting diseases with missing data.

Computer methods and programs in biomedicine·2023

Related Experiment Video

Updated: Feb 14, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.8K

Multi-Objectivising Combinatorial Optimisation Problems by Means of Elementary Landscape Decompositions.

Josu Ceberio1, Borja Calvo2, Alexander Mendiburu3

  • 1Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Donostia, 20018, Spain josu.ceberio@ehu.eus.

Evolutionary Computation
|February 16, 2018
PubMed
Summary

Multi-objective optimization algorithms, when applied to single-objective problems through elementary landscape decomposition, significantly outperform traditional single-objective methods. This approach enhances algorithm exploration for better combinatorial optimization results.

Keywords:
Multi-objectivisationcombinatorial optimisationelementary landscape decompositionmulti-objective evolutionary algorithm.

More Related Videos

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology
07:03

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Published on: December 1, 2023

1.5K
Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

3.1K

Related Experiment Videos

Last Updated: Feb 14, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.8K
Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology
07:03

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Published on: December 1, 2023

1.5K
Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

3.1K

Area of Science:

  • Combinatorial Optimization
  • Multi-objective Optimization
  • Computational Intelligence

Background:

  • Recent advances in multi-objective optimization (MOO) have spurred interest in applying MOO algorithms to single-objective problems (SOPs).
  • A common technique involves transforming SOPs into multi-objective problems (MOPs) to leverage MOO algorithms.

Purpose of the Study:

  • To present a general methodology for transforming SOPs into MOPs using elementary landscape decomposition.
  • To evaluate the effectiveness of this methodology on diverse combinatorial optimization problems.

Main Methods:

  • Developed a framework based on elementary landscape decomposition to create MOPs from SOPs.
  • Applied two well-known MOO algorithms, NSGA-II and SPEA2, to benchmark instances of four combinatorial problems: quadratic assignment, linear ordering, 0-1 unconstrained quadratic optimization, and frequency assignment.
  • Compared performance against a standard single-objective Genetic Algorithm (GA).

Main Results:

  • MOO algorithms (NSGA-II and SPEA2) demonstrated superior performance compared to the single-objective GA across all tested problem domains.
  • The multi-objective space generated by the decomposition enhanced the exploration capabilities of the algorithms.
  • NSGA-II and SPEA2 achieved better results on the majority of the problem instances.

Conclusions:

  • The elementary landscape decomposition methodology is effective for enhancing single-objective combinatorial optimization using multi-objective algorithms.
  • The enhanced exploration ability of the multi-objective space is key to the improved performance of NSGA-II and SPEA2.
  • This approach offers a promising direction for solving complex combinatorial optimization problems.