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Combating coevolutionary disengagement by reducing parasite virulence.

John Cartlidge1, Seth Bullock

  • 1Informatics Network, School of Computing, University of Leeds, LS2 9JT, UK. johnc@comp.leeds.ac.uk

Evolutionary Computation
|May 26, 2004
PubMed
Summary

Coevolutionary algorithms can struggle with disengagement. This study introduces a novel approach, inspired by host-parasite systems, to improve coevolutionary algorithm success by selecting for reduced virulence.

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Area of Science:

  • Artificial Intelligence
  • Evolutionary Computation
  • Machine Learning

Background:

  • Standard evolutionary algorithms use static fitness metrics.
  • Coevolutionary algorithms assess individuals against evolving opponents, offering benefits but facing challenges like disengagement.
  • Disengagement is a critical, yet under-explored, problem in coevolutionary systems.

Purpose of the Study:

  • To introduce a novel technique for addressing disengagement in coevolutionary algorithms.
  • To propose an alternative to maximizing adversarial ability by focusing on reduced virulence.
  • To demonstrate the effectiveness of this approach in improving coevolutionary algorithm success.

Main Methods:

  • Inspired by natural host-parasite dynamics.

Related Experiment Videos

  • Implemented a selection mechanism favoring reduced "virulence" instead of maximum opponent defeat.
  • Conducted experiments in both simple and complex domains to validate the technique.

    • Main Results:

    • The proposed method effectively mitigates disengagement in coevolutionary algorithms.
    • Selecting for reduced virulence proved more successful than maximizing competitive ability.
    • The technique demonstrated efficacy across diverse experimental domains.

    Conclusions:

    • Reduced virulence is a viable strategy to overcome disengagement in coevolutionary algorithms.
    • This host-parasite-inspired approach offers a novel solution to a persistent problem in evolutionary computation.
    • The findings suggest a promising direction for enhancing the robustness and success of coevolutionary systems.