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New methods for competitive coevolution.

C D Rosin1, R K Belew

  • 1Department of Computer Science and Engineering, University of California, San Diego, La Jolla 92093-0114, USA. crosin@cs.ucsd.edu

Evolutionary Computation
|April 1, 1997
PubMed
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This study explores competitive coevolution, where hosts and parasites evolve in an arms race. New techniques like competitive fitness sharing and shared sampling were tested on Nim and 3-D Tic-Tac-Toe.

Area of Science:

  • Evolutionary Biology
  • Artificial Intelligence
  • Game Theory

Background:

  • Competitive coevolution involves two populations, hosts and parasites, with fitness determined by direct competition.
  • This dynamic can lead to evolutionary arms races, driving reciprocal increases in complexity and performance.
  • Understanding these dynamics is crucial for fields ranging from ecology to AI development.

Purpose of the Study:

  • To introduce and evaluate novel techniques for enhancing competitive coevolutionary algorithms.
  • To investigate the impact of these techniques on the progression of evolutionary arms races.
  • To analyze the performance and diversity of evolving populations in competitive scenarios.

Main Methods:

  • Utilized the games of Nim and 3-D Tic-Tac-Toe as testbeds for competitive coevolution.

Related Experiment Videos

  • Introduced "competitive fitness sharing" to modify fitness evaluation.
  • Implemented "shared sampling" for selecting diverse parasite populations and a "hall of fame" to preserve high-performing individuals.

    • Main Results:

    • Experimental comparisons demonstrated the effectiveness of the new techniques.
    • Analysis revealed impacts on testing issues, population diversity, extinction rates, and the measurement of arms race progress.
    • The "hall of fame" method was shown to encourage sustained arms race progression.

    Conclusions:

    • The proposed techniques offer effective methods for managing and advancing competitive coevolution.
    • These advancements can lead to more robust and complex evolutionary outcomes in host-parasite systems.
    • The study provides mathematical insights and experimental validation for future research in coevolutionary algorithms.