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

Multiple-vector self-adaptation in evolutionary algorithms.

D B Fogel1, G B Fogel, K Ohkura

  • 1Natural Selection, Inc., 3333 N. Torrey Pines Ct., Suite 200, La Jolla, CA 92037, USA. dfogel@natural-selection.com

Bio Systems
|November 22, 2001
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

Predicted coreceptor usage at end-stage HIV disease in tissues derived from subjects on antiretroviral therapy with an undetectable plasma viral load.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases·2017
Same author

Interactions of Dietary Fats and Proteins on Fatty Acid Composition of Immune Cells and LTB4 Production by Peritoneal Exudate Cells of Rats.

Bioscience, biotechnology, and biochemistry·2016
Same author

A step toward computer-assisted mammography using evolutionary programming and neural networks.

Cancer letters·2008
Same author

Special section on machine intelligence approaches to systems biology.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2008
Same author

Evolving neural networks to play checkers without relying on expert knowledge.

IEEE transactions on neural networks·2008
Same author

Depression of sink activity precedes the inhibition of biomass production in tomato plants subjected to potassium deficiency stress.

Journal of experimental botany·2007
Same journal

The Quantum-to-Classical Transducer: A Thermodynamic and Quantum Mechanical Framework for the Emergence of Bioenergetics.

Bio Systems·2026
Same journal

Forward-backward gene expression binarization for boolean state inference over a known regulatory network.

Bio Systems·2026
Same journal

Partial-Label Metric Ceilings for Evaluating Gene Regulatory Networks Inferred from Single-Cell Foundation Models.

Bio Systems·2026
Same journal

The impedance mismatch theory: A non-equilibrium thermodynamic framework for a shared energetic stress pathway in neurodegeneration.

Bio Systems·2026
Same journal

Immune signal-status misclassification: A theoretical framework for biological status assignment and failed status resolution.

Bio Systems·2026
Same journal

Contextuality, incompatibility, and intra-system entanglement of mental markers: From cognition and decision making to medicine.

Bio Systems·2026
See all related articles

Using multiple strategy parameter vectors in evolutionary algorithms improves exploration and prevents search stagnation. This approach enhances the learning of online control parameters for better optimization performance.

Area of Science:

  • Evolutionary Computation
  • Optimization Algorithms
  • Machine Learning

Background:

  • Self-adaptation is key for online control parameter learning in evolutionary algorithms.
  • Individuals are often represented as (solution, strategy parameter) vectors, where strategy parameters guide offspring generation.
  • Small strategy parameters can lead to evolutionary search stagnation and inadequate exploration.

Purpose of the Study:

  • To investigate the effectiveness of using multiple strategy parameter vectors per individual.
  • To address the issue of search stagnation caused by small strategy parameters.

Main Methods:

  • A novel approach associating multiple strategy parameter vectors with a single individual was proposed.
  • Experiments were conducted on four 10-dimensional benchmark functions.

Related Experiment Videos

  • The number of strategy parameter vectors per individual was systematically varied (1, 2, 3, 4, 5, 10, 20).

    • Main Results:

    • Using multiple strategy parameter vectors demonstrated clear advantages in evolutionary search.
    • A reliable relationship was found between the number of strategy vectors and the mean best result achieved.
    • Improved exploration of response surfaces was observed with the proposed method.

    Conclusions:

    • Associating multiple strategy parameter vectors with individuals is a beneficial enhancement for evolutionary algorithms.
    • This method effectively mitigates search stagnation and improves optimization performance.
    • The study provides a reliable framework for determining optimal numbers of strategy vectors.