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

Self-evolution in a constructive binary string system

P Dittrich1, W Banzhaf

  • 1Department of Computer Science, University of Dortmund, D 44221 Dortmund, Germany. dittrich@LS11.informatik.uni-dortmund.de

Artificial Life
|December 16, 1998
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

Chemical computing with reaction-diffusion processes.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2015
Same author

Carbon dioxide fixation by epidermal and mesophyll tissues of Tulipa and Commelina.

Planta·2014
Same author

[(14)C]Carbon-dioxide fixation by isolated leaf epidermes with stomata closed or open.

Planta·2014
Same author

Malate metabolism in isolated epidermis of Commelina communis L. in relation to stomatal functioning.

Planta·2014
Same author

Uptake and metabolism of carbohydrates by epidermal tissue.

Planta·2014
Same author

Inhibition of stomatal opening during uptake of carbohydrates by guard cells in isolated epidermal tissues.

Planta·2014
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

This study introduces a self-evolving system of binary strings, inspired by chemical processes. The system demonstrates evolution without external operators, as strings process each other to create new variations.

Area of Science:

  • Computational systems biology
  • Theoretical computer science
  • Information theory

Background:

  • Catalytic self-organizing systems are complex adaptive systems.
  • Binary strings can represent data or processing machines.
  • Chemical information processing offers a metaphor for understanding complex systems.

Purpose of the Study:

  • To investigate the qualitative dynamics of a catalytic self-organizing system composed of binary strings.
  • To explore emergent evolutionary phenomena in a system without explicit mutation, recombination, or selection.
  • To analyze the concept of self-evolution where system components drive their own variations.

Main Methods:

  • Modeling a system where binary strings function as both data and machines.

Related Experiment Videos

  • Interpreting strings as data for processing and as machines for executing operations.
  • Observing system dynamics without predefined evolutionary operators.

    • Main Results:

    • The system exhibits emergent evolutionary dynamics.
    • Variations and adaptations arise intrinsically from the interactions between string-as-machine and string-as-data.
    • The system demonstrates self-organization and adaptation without external intervention.

    Conclusions:

    • Catalytic self-organizing systems of binary strings can exhibit self-evolution.
    • The dual role of binary strings (data and machine) is crucial for emergent evolution.
    • This model provides insights into intrinsic evolutionary mechanisms in artificial and potentially natural systems.