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Evolutionary Implications of Self-Assembling Cybernetic Materials with Collective Problem-Solving Intelligence at

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This study shows that evolving complex biological systems with decision-making agents (multi-scale competency architecture) accelerates evolution more than direct pattern evolution. These evolved systems exhibit robust adaptability.

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Area of Science:

  • Developmental biology
  • Evolutionary biology
  • Computational neuroscience

Background:

  • Biology features a complex multi-scale competency architecture (MCA) with nested homeostatic agents.
  • How natural selection creates MCAs is a key research question.
  • This study examines MCA's impact on evolution, not its origin.

Purpose of the Study:

  • Investigate how decision-making competencies of MCA agents affect evolution.
  • Model morphogenesis using neural cellular automata (NCAs) and evolutionary algorithms.
  • Analyze the effect of varying cellular agent regulation accuracy on evolution.

Main Methods:

  • Used in silico neuroevolution experiments with simulated minimal developmental biology.
  • Modeled morphogenesis with neural cellular automata (NCAs).
  • Employed an evolutionary algorithm to optimize NCA parameters for self-assembling a 2D pattern.

Main Results:

  • Evolutionary processes were significantly faster when optimizing MCA functional parameters versus direct pattern evolution.
  • Evolved MCAs demonstrated strong generalization to system parameter changes and modified objectives.
  • Agent competency levels were scaled from direct encoding to MCA.

Conclusions:

  • Adaptive problem-solving competencies of agential parts in NCAs significantly influence the evolutionary process.
  • The findings suggest functional implications of near-ubiquitous competency in living matter.
  • MCA facilitates more rapid and robust evolutionary adaptation.