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Related Experiment Videos

Cooperative coevolution: an architecture for evolving coadapted subcomponents.

M A Potter1, K A De Jong

  • 1Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Washington, DC 20375, USA. mpotter@aic.nrl.navy.mil

Evolutionary Computation
|April 7, 2000
PubMed
Summary
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Evolving cooperating species enables emergent, interdependent subcomponents for complex problems. This approach facilitates adaptation and niche coverage in evolutionary algorithms.

Area of Science:

  • Artificial Intelligence
  • Evolutionary Computation
  • Computational Neuroscience

Background:

  • Complex problems require solutions composed of interacting, coadapted subcomponents.
  • Current evolutionary algorithms often rely on hand-designed subcomponents, limiting scalability.
  • Developing computational methods for emergent subcomponent evolution is crucial.

Purpose of the Study:

  • To introduce an architecture for evolving cooperating species to generate emergent subcomponents.
  • To demonstrate how evolutionary pressure can drive the emergence of interdependent subcomponents.
  • To explore the application of this architecture in evolving artificial neural networks.

Main Methods:

  • Proposed an architecture for evolving solutions as a collection of cooperating species.

Related Experiment Videos

  • Utilized a string-matching task to demonstrate the emergence of subcomponents.
  • Conducted a case study involving the evolution of artificial neural networks.

    • Main Results:

    • Evolutionary pressure successfully stimulated the emergence of interdependent subcomponents.
    • Subcomponents evolved to cover multiple niches and appropriate generality.
    • The system demonstrated adaptability to changing subcomponent numbers and roles.
    • Successful application in evolving artificial neural networks.

    Conclusions:

    • The cooperating species architecture effectively promotes the emergence of coadapted subcomponents.
    • This paradigm offers a computational extension for tackling complex problems with evolutionary algorithms.
    • The approach shows promise for evolving sophisticated structures like artificial neural networks.