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Measuring Microbial Mutation Rates with the Fluctuation Assay
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Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and

Santiago Hernández-Orozco1,2,3, Narsis A Kiani2,3, Hector Zenil2,3

  • 1Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México (UNAM), Mexico.

Royal Society Open Science
|September 19, 2018
PubMed
Summary

This study introduces an algorithmic mutation model that accelerates evolutionary rates, potentially explaining major evolutionary events like diversity explosions and mass extinctions. This computational approach offers a more accurate model of biological evolution than random mutation models.

Keywords:
algorithmic probabilityalgorithmic randomnessartificial evolutionartificial lifebiological evolutionmetabiology

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Area of Science:

  • Evolutionary Biology
  • Computational Biology
  • Theoretical Biology

Background:

  • Natural selection is a key driver of evolution, but random mutation models struggle to explain observed evolutionary speeds.
  • Previous models often assume uniform mutation distributions, which may not reflect natural processes.

Purpose of the Study:

  • To investigate the impact of algorithmic mutation distributions on evolutionary convergence rates.
  • To compare evolutionary dynamics under algorithmic versus uniform mutation models.
  • To explore the potential of algorithmic mutations to explain major evolutionary events and drive artificial evolution.

Main Methods:

  • Applied a simplicity bias with algorithmic mutation distributions (no recombination) to binary matrices and small biological examples.
  • Compared evolutionary convergence rates between algorithmic and statistically uniform mutation models.
  • Analyzed the emergence of modularity, genetic memory, and population dynamics.

Main Results:

  • Algorithmic mutation distributions significantly accelerated evolutionary convergence rates compared to uniform distributions.
  • Algorithmic distributions promoted the evolution of modularity and genetic memory.
  • Observed phenomena including accelerated diversity, population extinctions, and potential explanations for mass extinctions and diversity explosions.
  • Demonstrated accelerated convergence in artificial evolutionary algorithms.

Conclusions:

  • Algorithmic mutation distributions provide a more accurate approximation of biological evolution than random uniform models.
  • This computational approach may explain major evolutionary events and open-ended evolution.
  • Computation is a significant driver of evolution, impacting both natural and artificial systems.