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

Covariance matrix adaptation for multi-objective optimization.

Christian Igel1, Nikolaus Hansen, Stefan Roth

  • 1Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum, Germany. christian.igel@neuroinformatik.rub.de

Evolutionary Computation
|March 29, 2007
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-resolution automated mapping of potential Aedes larval container habitats using drone imagery and supervised machine learning in Dar es Salaam, Tanzania.

PLoS neglected tropical diseases·2026
Same author

Global daily 9 km remotely sensed soil moisture (2015-2025) with microwave radiative transfer-guided learning.

Scientific data·2026
Same author

The stroke risk gene Foxf2 maintains brain endothelial cell function via Tie2 signaling.

Nature neuroscience·2025
Same author

Absolute and relative risks of mental disorders in families: a Danish register-based study.

The lancet. Psychiatry·2025
Same author

Improving the Real-Time Classification of Disease Severity in Ulcerative Colitis: Artificial Intelligence as the Trigger for a Second Opinion.

The American journal of gastroenterology·2025
Same author

A cis-regulatory element controls expression of histone deacetylase 9 to fine-tune inflammasome-dependent chronic inflammation in atherosclerosis.

Immunity·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

A new multi-objective optimization algorithm (MO-CMA-ES) enhances the powerful covariance matrix adaptation evolution strategy (CMA-ES). This variant improves performance on complex problems, outperforming existing multi-objective algorithms.

Area of Science:

  • Optimization Algorithms
  • Evolutionary Computation
  • Multi-Objective Optimization

Background:

  • Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a leading algorithm for single-objective optimization.
  • Existing multi-objective optimization (MOO) algorithms face challenges in complex search spaces.

Purpose of the Study:

  • To develop a novel variant of CMA-ES for multi-objective optimization (MOO).
  • To evaluate the performance of the new MO-CMA-ES against established MOO algorithms.

Main Methods:

  • Introduced an elitist single-objective CMA-ES variant with plus-selection and success-rule-based step size control.
  • Developed the multi-objective CMA-ES (MO-CMA-ES) incorporating non-dominated sorting with crowding-distance or hypervolume as secondary criteria.
  • Inherited invariance properties, including rotation invariance, from the original CMA-ES.

Related Experiment Videos

Main Results:

  • The elitist single-objective CMA-ES showed slight speed improvements on unimodal functions but increased susceptibility to local minima.
  • The MO-CMA-ES demonstrated superior performance compared to NSGA-II and NSDE in experimental evaluations.
  • Both developed algorithms retained key invariance properties of the original CMA-ES.

Conclusions:

  • The proposed MO-CMA-ES is a promising advancement for multi-objective optimization.
  • The algorithm effectively handles complex, real-valued optimization problems.
  • MO-CMA-ES offers a competitive alternative to existing state-of-the-art MOO methods.