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Videos de Conceptos Relacionados

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Evolutionary Processes in Microbes01:26

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Microbial evolution occurs rapidly due to short generation times and a variety of genetic processes, including horizontal gene transfer, mutation, recombination, and genetic drift. These mechanisms collectively enable microbes to adapt swiftly to changing environments.Horizontal gene transfer (HGT) allows genes to move between different species and occurs through three main mechanisms: conjugation, transformation, and transduction. Conjugation involves direct cell-to-cell contact for DNA...
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Convergent Evolution01:54

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Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.
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What is Evolutionary History?02:35

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Scientists record evolutionary history by analyzing fossil, morphological, and genetic data. The fossil record documents the history of life on Earth and provides evidence for evolution. However, both fossil and living organisms offer evidence that outlines Earth’s evolutionary history.
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Gene Evolution - Fast or Slow?02:05

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Desde la computación evolutiva hasta la evolución de las cosas.

Agoston E Eiben1, Jim Smith2

  • 1VU University Amsterdam, de Boelelaan 1081a, 1081HV Amsterdam, the Netherlands.

Nature
|May 29, 2015
PubMed
Resumen
Este resumen es generado por máquina.

La computación evolutiva, inspirada en la evolución natural, resuelve problemas complejos de ingeniería. Nuevos algoritmos basados en hardware permiten máquinas autónomas adaptativas, fusionando la evolución artificial con los sistemas físicos.

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Área de la Ciencia:

  • Ciencias de la computación Ciencias de la computación
  • La inteligencia artificial es inteligencia artificial.
  • La computación evolutiva de la computación

Sus antecedentes:

  • La computación evolutiva se inspira en la evolución natural para el diseño de algoritmos.
  • Tiene un historial comprobado en la resolución de diversos desafíos de ingeniería.
  • El campo está avanzando con las implementaciones de hardware.

Objetivo del estudio:

  • Para comparar el cálculo evolutivo con la evolución natural.
  • Para resaltar los beneficios de la computación evolutiva sobre otros métodos de computación.
  • Introducir la evolución artificial en los sistemas físicos.

Principales métodos:

  • Análisis comparativo de la computación evolutiva y la evolución natural.
  • Revisión de los algoritmos evolutivos existentes y sus aplicaciones.
  • Exploración de algoritmos evolutivos basados en hardware.

Principales resultados:

  • La computación evolutiva ofrece un enfoque poderoso para la resolución de problemas complejos.
  • Las implementaciones de hardware allanan el camino para las máquinas autónomas adaptativas.
  • La evolución artificial en los sistemas físicos representa un área emergente significativa.

Conclusiones:

  • La computación evolutiva es un paradigma computacional versátil y eficaz.
  • La integración del hardware es crucial para el desarrollo de sistemas inteligentes adaptables.
  • La evolución artificial en los sistemas físicos tiene un potencial futuro sustancial.