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What is Evolutionary History?02:35

What is Evolutionary History?

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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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Evolutionary Psychology01:20

Evolutionary Psychology

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Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
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Uniform Distribution01:19

Uniform Distribution

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The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.
Two essential properties of this distribution are
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Criticisms of the Evolutionary Perspective01:23

Criticisms of the Evolutionary Perspective

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In a study where individuals posing as strangers offered compliments and proposed casual sex to students, the responses differed significantly based on gender. Not a single woman accepted the proposal, while 70% of the men agreed. This outcome provides a useful scenario to explore through the lens of evolutionary psychology and social learning theory, highlighting the diverse perspectives on human sexual behaviors.
Evolutionary psychology provides one explanation for these findings, suggesting...
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Uniform Circular Motion01:14

Uniform Circular Motion

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Uniform circular motion is a specific type of motion in which an object travels in a circle with a constant speed. For example, any point on a propeller spinning at a constant rate is undergoing uniform circular motion. The second, minute, and hour hands of a watch also undergo uniform circular motion. It is hard to believe that points on these rotating objects are actually accelerating, even though the rotation rate is constant. To understand this, we must analyze the motion in terms of...
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Non-uniform Circular Motion01:22

Non-uniform Circular Motion

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In uniform circular motion, the particle executing circular motion has a constant speed, and the circle is at a fixed radius. However, not all circular motion occurs at a constant speed. A particle can travel in a circle and speed up or slow down, showing an acceleration in the direction of motion. In that case, the motion is called non-uniform circular motion, and an additional acceleration is introduced, which is in the direction tangential to the circle. 
For example, such...
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Updated: Feb 12, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Un algoritmo evolutivo multiobjetivo basado en la bipoblación con muestreo uniforme para la búsqueda de arquitectura

Yu Xue, Pengcheng Jiang, Chenchen Zhu

    IEEE transactions on neural networks and learning systems
    |February 10, 2026
    PubMed
    Resumen
    Este resumen es generado por máquina.

    Este estudio presenta MOEA-BUS, un nuevo algoritmo para la búsqueda de arquitectura neural (NAS) que optimiza tanto la precisión como la complejidad de la red. Mejora la diversidad de la población y la cobertura del espacio de búsqueda para un rendimiento superior.

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    Área de la Ciencia:

    • La inteligencia artificial es inteligencia artificial.
    • Aprendizaje automático Aprendizaje automático.
    • Ciencias de la computación Ciencias de la computación

    Sus antecedentes:

    • La Búsqueda de Arquitectura Neural (NAS) automatiza el diseño de redes neuronales, pero se enfrenta a desafíos en la optimización de múltiples objetivos como la precisión y la complejidad.
    • Los métodos NAS existentes a menudo carecen de diversidad de población y una exploración espacial de búsqueda adecuada, especialmente en regiones complejas.

    Objetivo del estudio:

    • Proponer un nuevo algoritmo evolutivo multiobjetivo (MOEA) -BUS para NAS que optimice simultáneamente la precisión y la complejidad de las redes neuronales.
    • Mejorar la eficiencia y efectividad de los NAS mejorando la diversidad de la población y la cobertura del espacio de búsqueda.

    Principales métodos:

    • Desarrolló MOEA-BUS, un algoritmo evolutivo multiobjetivo que utiliza un marco de bipopulación y un nuevo método de muestreo uniforme para la inicialización de la población.
    • Evolución sinérgica implementada entre dos poblaciones para garantizar una cobertura completa del espacio de búsqueda.
    • Realizó experimentos en conjuntos de datos CIFAR-10 e ImageNet para evaluar el rendimiento.

    Principales resultados:

    • MOEA-BUS logró una precisión superior del 98.39% en CIFAR-10 y del 80.03% en ImageNet.
    • Logró una precisión del 78.28% en ImageNet con una baja complejidad de red de 446 M MAdds.
    • Los estudios de ablación confirmaron que el muestreo uniforme y los mecanismos de bipoblación mejoran significativamente la diversidad de la población y el rendimiento general.

    Conclusiones:

    • MOEA-BUS optimiza efectivamente tanto la precisión como la complejidad de la red en NAS, superando a los métodos existentes.
    • Las estrategias de muestreo uniforme y de bipoblación propuestas son cruciales para mejorar la diversidad y el rendimiento en NAS.
    • El algoritmo demuestra un gran potencial para el diseño de redes neuronales eficientes y de alto rendimiento.