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Global progress in competitive co-evolution: a systematic comparison of alternative methods.

Stefano Nolfi1, Paolo Pagliuca1

  • 1Laboratory of Autonomous Robotics and Artificial Life (LARAL), Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Rome, Italy.

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|February 5, 2025
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Summary

Competitive co-evolution enhances adaptive agents but can cause strategy cycling. This study introduces methods to measure progress, with the Generalist method demonstrating superior long-term global advancement in agent evolution.

Keywords:
competitive co-evolutionevolutionary roboticslocal historical and global progressopen-ended evolutionpredator-prey robots

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Evolutionary Computation

Background:

  • Competitive co-evolution drives adaptation in artificial agents by simulating environmental variations.
  • A common challenge is the emergence of limit cycles, where agents repeatedly cycle through strategy discovery and forgetting.

Purpose of the Study:

  • To investigate methods for synthesizing progressively better solutions using competitive co-evolution.
  • To introduce and evaluate techniques for measuring historical and global progress in agent evolution.
  • To identify factors that facilitate genuine progress and overcome local optima.

Main Methods:

  • Developed methods to measure historical and global progress in competitive co-evolution.
  • Implemented algorithms that create archives of diverse opponents for evaluating agents.
  • Introduced techniques to identify and discard strategies leading only to local progress.
  • Compared four algorithms, including two novel methods, in a predator-prey simulation.

Main Results:

  • All tested algorithms achieved long-term global progress in the predator-prey scenario.
  • Significant variations were observed in the rate of progress and the ratio of progress to retrogressions among algorithms.
  • The novel Generalist method consistently outperformed other algorithms, uniquely enabling sustained global progress.

Conclusions:

  • Effective progress measurement and opponent archiving are crucial for successful competitive co-evolution.
  • The Generalist method offers a robust approach to achieving genuine and sustained global progress in adaptive agent evolution.
  • Careful algorithm design is necessary to mitigate limit cycles and promote consistent advancement.