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 Experiment Videos

Adaptive linkage crossover.

A A Salman1, K Mehrotra, C K Mohan

  • 1Department of ECE, Kuwait University. ayed@eng.kuniv.edu.kw

Evolutionary Computation
|September 23, 2000
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

High- versus low-dose proton pump inhibitors post endoscopic hemostasis in hemodialysis cases with peptic ulcer bleeding.

Acta gastro-enterologica Belgica·2021
Same author

Long-term outcome after endoscopic ligation of acute esophageal variceal bleeding in patients with liver cirrhosis.

Acta gastro-enterologica Belgica·2020
Same author

Nano-indentation and laser-induced damage testing in optical multilayer-dielectric gratings.

Applied optics·2017
Same author

Nano-indentation of single-layer optical oxide thin films grown by electron-beam deposition.

Applied optics·2015
Same author

Fracture mechanics of delamination defects in multilayer dielectric coatings.

Applied optics·2013
Same author

Co(II), Ce(III) and UO2VI) bis-salicylatothiosemicarbazide complexes: binary and ternary complexes, thermal studies and antimicrobial activity.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2004
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

This study introduces a novel linkage-based crossover operator for genetic algorithms (GAs). This method leverages inter-component relationships to improve search efficiency and overcome representation limitations in complex problems.

Area of Science:

  • Artificial Intelligence
  • Computational Biology
  • Optimization Algorithms

Background:

  • Heuristics guide search algorithms, including genetic algorithms (GAs), towards optimal solutions.
  • Problem-specific knowledge is crucial for efficient search, but often lies in component interrelations rather than individual components.

Purpose of the Study:

  • To develop a novel crossover operator for genetic algorithms (GAs) that utilizes inter-component relationships (linkages).
  • To free GAs from the constraints of fixed problem representations by exploiting linkage information.

Main Methods:

  • Development of an interrelation (linkage) based crossover operator.
  • Representation of linkage strength using a linkage matrix.
  • Application of the linkage matrix in the reproduction step to generate new individuals.

Related Experiment Videos

  • Exploration of both a priori known and learned linkage matrices during evolutionary algorithm execution.

    • Main Results:

    • Demonstrated success of the linkage-based crossover operator across various problems.
    • The approach liberates GAs from fixed representation constraints.
    • Explicit representation of component linkages enhances search efficiency.

    Conclusions:

    • Linkage-based crossover operators offer a powerful mechanism to improve GA performance by exploiting problem-specific inter-component knowledge.
    • This method provides a flexible framework adaptable to problems with known or learnable linkage structures.