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Maximum Individual Complexity is Indefinitely Scalable in Geb.

Alastair Channon1

  • 1Keele University, School of Computing and Mathematics. a.d.channon@keele.ac.uk.

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Summary
This summary is machine-generated.

The artificial life system Geb demonstrates open-ended evolution. Maximum individual complexity can scale indefinitely when both population size and neuron count parameters are increased together.

Keywords:
Open-ended evolutionbiotic selectiondiversityindefinite scalabilityongoing growth of complexity

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Area of Science:

  • Artificial life research
  • Evolutionary computation
  • Complexity science

Background:

  • Geb is the first artificial life system classified with open-ended evolutionary dynamics.
  • Its evolution is driven by biotic selection (natural selection).

Purpose of the Study:

  • To evaluate if Geb can achieve indefinite increases in maximum individual complexity.
  • To investigate the impact of scaling population size and neuron count on complexity growth.

Main Methods:

  • Scaling world length (population size) and maximum neurons per individual.
  • Analyzing complexity growth under different parameter combinations.
  • Conducting long-term simulations (years, billions of reproductions).

Main Results:

  • Maximum individual complexity is asymptotically bounded when scaling only one parameter.
  • Indefinite scalability of maximum individual complexity is achieved when scaling both parameters together.
  • Complexity scales logarithmically with the minimum of population size and neuron count.

Conclusions:

  • Open-ended evolutionary systems can achieve indefinitely scalable complexity.
  • Co-scaling population size and neuron count is key for complexity growth.
  • Findings inform strategies for enhancing complexity in artificial evolutionary systems.