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

On the Baldwin effect.

L Bull1

  • 1Faculty of Computer Studies and Mathematics, University of the West of England, Bristol BS16 1QY, U.K. larry@ics.uwe.ac.uk

Artificial Life
|January 29, 2000
PubMed
Summary
This summary is machine-generated.

This study explores how learning rate and amount impact the Baldwin effect using a tunable NK model. Findings show adaptation benefits from faster learning on less correlated landscapes and more learning at higher rates on uncorrelated landscapes.

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

Primary cervical lymphoma: a rare presentation to a genitourinary medicine clinic.

International journal of STD & AIDS·2013
Same author

Temper outbursts in Prader-Willi syndrome: causes, behavioural and emotional sequence and responses by carers.

Journal of intellectual disability research : JIDR·2013
Same author

Analysis of sequence configurations of the PKR-interacting HCV proteins from plasma and PBMC as predictors of response to interferon-alpha and ribavirin therapy in HIV-coinfected patients.

Intervirology·2008
Same author

Assignment of the sperm protein zona receptor tyrosine kinase gene (SPRMTK) to porcine chromosome SSC3q11-->q12 by fluorescence in situ hybridization and by analysis of somatic cell and radiation hybrid panels.

Cytogenetic and genome research·2003
Same author

Symbiogenesis in learning classifier systems.

Artificial life·2001
Same author

On meme--gene coevolution.

Artificial life·2001
Same journal

If Turing Played Piano With an Artificial Partner.

Artificial life·2026
Same journal

Discovering Partial Differential Equations With Neural Cellular Automata.

Artificial life·2026
Same journal

Book Review: Exploring the Boundaries of Life-as-It-Is.

Artificial life·2026
Same journal

System 0/1/2/3: Quad-Process Theory for Multitimescale Embodied Collective Cognitive Systems.

Artificial life·2025
Same journal

To Engineer an Angel, First Validate the Devil: Analyzing the "Could Be" in Artificial Life's "Life as-It-Could-Be".

Artificial life·2025
Same journal

Untapped Potential in Self-Optimization of Hopfield Networks: The Creativity of Unsupervised Learning.

Artificial life·2025
See all related articles

Area of Science:

  • Evolutionary biology
  • Computational modeling
  • Artificial intelligence

Background:

  • The Baldwin effect describes how learned behaviors can influence evolutionary trajectories.
  • Understanding the parameters that modulate the Baldwin effect is crucial for evolutionary theory.
  • Computational models offer a powerful tool to investigate complex evolutionary dynamics.

Purpose of the Study:

  • To investigate the impact of learning rate and learning amount on the Baldwin effect.
  • To determine how these learning parameters interact with fitness landscape correlations.
  • To identify conditions under which adaptation is enhanced via the Baldwin effect.

Main Methods:

  • Utilized a tunable NK model, a computational framework for simulating evolutionary processes.

Related Experiment Videos

  • Systematically varied the rate and amount of learning within the model.
  • Analyzed the adaptation process across different fitness landscape correlation levels.

    • Main Results:

    • The adaptation process demonstrated sensitivity to the learning rate, especially with decreasing landscape correlation.
    • A high learning rate was generally most beneficial as landscape correlation decreased.
    • Increased amounts of learning proved advantageous under higher learning rates on uncorrelated landscapes.

    Conclusions:

    • Learning rate and amount are significant factors influencing the Baldwin effect.
    • The interplay between learning parameters and landscape properties shapes adaptive evolution.
    • These findings provide insights into the mechanisms by which learned behaviors can guide evolutionary change.