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Long-Term Progress and Behavior Complexification in Competitive Coevolution.

Luca Simione1, Stefano Nolfi2,3

  • 1Institute of Cognitive Sciences and Technologies, National Research Council (CNR-ISTC). luca.simione@istc.cnr.it.

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|December 7, 2020
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Summary
This summary is machine-generated.

This study introduces a novel competitive algorithm to overcome limitations in evolutionary algorithms, enabling long-term global progress by filtering opportunistic variations. The method ensures sustained advancement rather than cycles of local improvements.

Keywords:
Competitive coevolutionarms racesbehavior complexitylong-term progress

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Area of Science:

  • Artificial Intelligence
  • Evolutionary Computation
  • Robotics

Background:

  • Competitive evolutionary algorithms often stall due to limit cycle dynamics.
  • Evolving agents may achieve local progress but fail to reach global optima.

Purpose of the Study:

  • To propose a new competitive algorithm for achieving long-term global progress.
  • To address the limitations of opportunistic variations in evolutionary systems.

Main Methods:

  • Developed a novel competitive algorithm that identifies and filters opportunistic variations.
  • Validated the algorithm's efficacy using the coevolution of predator and prey robots.

Main Results:

  • The proposed algorithm successfully produces long-term global progress.
  • Evolving robots demonstrated articulated behaviors and increased behavioral complexity over generations.

Conclusions:

  • The new algorithm effectively overcomes the convergence on limit cycles.
  • This approach facilitates sustained progress in complex coevolutionary systems.