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

The cooperative coevolutionary (1+1) EA.

Thomas Jansen1, R Paul Wiegand

  • 1Fachbereich Informatik, Universität Dortmund, 44221 Dortmund, Germany. Thomas.Jansen@udo.edu

Evolutionary Computation
|March 17, 2005
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

MMW Fortschritte der Medizin·2026
Same author

MMW Fortschritte der Medizin·2026
Same author

Mechanical circulatory support for cardiogenic shock in takotsubo syndrome.

Clinical research in cardiology : official journal of the German Cardiac Society·2026
Same author

Hemorrhagic complications in a pelvic kidney: the role of interventional radiology: A case report.

Radiology case reports·2025
Same author

MMW Fortschritte der Medizin·2025
Same author

MMW Fortschritte der Medizin·2025
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

Cooperative coevolutionary algorithms offer advantages for complex optimization tasks. However, their performance depends on function properties like separability, with potential drawbacks for certain inseparable functions.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Coevolutionary algorithms are advanced variants of evolutionary algorithms.
  • They are often preferred for complex tasks over noncoevolutionary methods.
  • Cooperative coevolutionary frameworks are a notable example.

Purpose of the Study:

  • To provide a rigorous analysis of cooperative coevolution's optimization potential.
  • To investigate the CC (1+1) EA within a cooperative coevolutionary framework.
  • To examine the role of function separability in algorithm performance.

Main Methods:

  • Defining and analyzing the CC (1+1) EA.
  • Focusing on expected optimization time as a performance metric.
  • Investigating objective functions based on their separability properties.

Related Experiment Videos

Main Results:

  • Separability alone does not guarantee an advantage for the CC (1+1) EA over noncoevolutionary counterparts.
  • The CC (1+1) EA's advantage stems from enhanced exploration capabilities.
  • For some inseparable functions, the CC (1+1) EA may fail to find global optima, even with infinite time.
  • The CC (1+1) EA can perform comparably or even better than traditional methods on certain inseparable functions.

Conclusions:

  • The effectiveness of cooperative coevolutionary algorithms is nuanced and depends on specific function characteristics.
  • While offering potential benefits through increased exploration, cooperative coevolution can also introduce challenges.
  • Careful consideration of function properties, particularly separability, is crucial when selecting optimization algorithms.