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The Evidence for Evolution02:55

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Genetic variations accumulating within populations over generations give rise to biological evolution. Evolutionary changes can result in the formation of novel varieties and entire new species. These changes are responsible for the diverse forms of life inhabiting the planet. The evidence for evolution suggests that all living organisms descended from common ancestors.
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The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
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Molecular Evolution of the Tre Recombinase
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Two Modes of Evolution: Optimization and Expansion.

Steen Rasmussen1, Paolo Sibani2

  • 1University of Southern Denmark, Center for Fundamental Living ,Technology (FLinT), Department for Physics, Chemistry, and PharmacySanta Fe Institute. steen@sdu.dk.

Artificial Life
|April 2, 2019
PubMed
Summary
This summary is machine-generated.

Evolution occurs via optimization or expansion. Optimization dynamics in physics, biology, and engineering result from independent events. Human culture and technology show combined optimization and expansion, with a unique accelerating variable.

Keywords:
Evolutionbiologylearningoptimizationphysicstechnology

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Area of Science:

  • Evolutionary dynamics
  • Complex systems
  • Human culture and technology

Background:

  • Two primary modes of evolution exist: optimization and expansion.
  • Optimization applies to stable systems, while expansion is crucial for open-ended evolution with new elements.
  • Understanding these modes is key to analyzing system development across various fields.

Purpose of the Study:

  • To document and discuss two modes of evolution: optimization and expansion.
  • To investigate evolutionary optimization dynamics in physics, biology, and engineering.
  • To explore the evolution of human culture and technology using economic data.

Main Methods:

  • Analyzing systems from physics, biology, and engineering to understand optimization dynamics.
  • Deriving a microscopic theory for optimization processes, supported by simulation results.
  • Separating and quantifying optimization and expansion in human evolution by transforming the independent variable using empirical economic data.

Main Results:

  • Evolutionary optimization dynamics are cumulative effects of independent events ('quakes') distributed on a logarithmic time scale.
  • A decelerating fitness improvement is observed when using appropriate independent variables (e.g., time, generations, units produced).
  • Human cultural and technological evolution exhibits a combined optimization and expansion process, with a transformed independent variable increasing faster than exponential time.

Conclusions:

  • Optimization dynamics in various systems can be explained by a microscopic theory.
  • Human evolution demonstrates a combined optimization and expansion, with an accelerating independent variable likely due to increased interactions and knowledge.
  • A microscopic theory for the time dependence of human evolution remains an open challenge.