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

Genetic diversity as an objective in multi-objective evolutionary algorithms.

Andrea Toffolo1, Ernesto Benini

  • 1Department of Mechanical Engineering, University of Padova Via Venezia 1, 35131 Padova, Italy. andrea.toffolo@unipd.it

Evolutionary Computation
|July 24, 2003
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

Experimental investigation of multi-step airfoils in low Reynolds numbers applications.

Heliyon·2024
Same author

Game theory-based analysis of policy instrument consequences on energy system actors in a Nordic municipality.

Heliyon·2024
Same author

Microalgal growth, nitrogen uptake and storage, and dissolved oxygen production in a polyculture based-open pond fed with municipal wastewater in northern Sweden.

Chemosphere·2021
Same author

Black liquor fractionation for biofuels production - a techno-economic assessment.

Bioresource technology·2014
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 the Genetic Diversity Evaluation Method (GeDEM) to enhance multi-objective evolutionary algorithms. GeDEM improves population diversity, leading to superior performance in evolutionary computation.

Area of Science:

  • Computational Intelligence
  • Evolutionary Computation
  • Optimization Algorithms

Background:

  • Maintaining genetic diversity is crucial for efficient multi-objective evolutionary algorithms.
  • Existing methods may not adequately balance exploitation and exploration.
  • Novel approaches are needed to improve algorithm reliability and performance.

Purpose of the Study:

  • To introduce a new diversity-preserving mechanism called the Genetic Diversity Evaluation Method (GeDEM).
  • To develop a novel multi-objective evolutionary algorithm, the Genetic Diversity Evolutionary Algorithm (GDEA), based on GeDEM.
  • To evaluate the effectiveness of GDEA against state-of-the-art algorithms.

Main Methods:

  • GeDEM incorporates a distance-based measure of genetic diversity as a fitness objective.

Related Experiment Videos

  • This creates dual selection pressure for exploiting current solutions and exploring the search space.
  • GDEA was implemented and tested on a standard suite of multi-objective problems.

    • Main Results:

    • The Genetic Diversity Evolutionary Algorithm (GDEA) demonstrated top-level performance.
    • GeDEM effectively enhances genetic diversity within the population.
    • The dual selection pressure contributed to improved search capabilities.

    Conclusions:

    • GeDEM is an effective mechanism for preserving genetic diversity in evolutionary algorithms.
    • GDEA offers a competitive and robust approach for multi-objective optimization.
    • The findings highlight the importance of explicit diversity management in evolutionary computation.