Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

147
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
147
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
100
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.9K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
1.9K
Reinforcement Schedules01:24

Reinforcement Schedules

239
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
239
Reinforcement01:23

Reinforcement

341
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
341
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.3K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.3K

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Joule-heating synthesis of high-entropy oxides as efficient catalysts for electrochemical methanol oxidation.

Chemical communications (Cambridge, England)·2026
Same author

Mesonephric-like adenocarcinoma of the uterine corpus: a case report.

Frontiers in medicine·2026
Same author

WNT4 reprograms dental pulp stem cells to resist PANoptosis and rebuild neurogenic potential for facial nerve injury repair.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]·2026
Same author

Monte Carlo investigation of spatiotemporal distortions in attosecond soft X-ray pulse focusing using a two-stage toroidal mirror system.

Optics express·2026
Same author

Integrated Analysis Identifies an Anoikis-Related Gene Signature for Predicting Prognosis in Patients With Triple-Negative Breast Cancer.

IET systems biology·2026
Same author

Multimodal interventional bronchoscopy for chronic pulmonary <i>Aspergillus</i> infection with post-tubercular bronchial occlusion: a case report.

Frontiers in medicine·2026

Video Experimental Relacionado

Updated: Sep 8, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

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

Published on: December 9, 2012

13.0K

Configuración automatizada de algoritmos evolutivos a través de aprendizaje de refuerzo profundo para optimización

Fei Ming, Wenyin Gong, Bing Xue

    IEEE transactions on cybernetics
    |September 5, 2025
    PubMed
    Resumen
    Este resumen es generado por máquina.

    Este estudio introduce el diseño de algoritmos automatizados para algoritmos evolutivos de optimización multiobjetivo restringidos (CMOEA) utilizando aprendizaje de refuerzo profundo (DRL). El nuevo enfoque aprende por sí mismo configuraciones óptimas, superando a los métodos tradicionales.

    Más Videos Relacionados

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
    11:53

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    11.8K
    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

    Published on: February 6, 2020

    9.5K

    Videos de Experimentos Relacionados

    Last Updated: Sep 8, 2025

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
    11:53

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

    Published on: December 9, 2012

    13.0K
    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
    11:53

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    11.8K
    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

    Published on: February 6, 2020

    9.5K

    Área de la Ciencia:

    • Cálculo evolutivo
    • Inteligencia artificial
    • Algoritmos de optimización

    Sus antecedentes:

    • El diseño automatizado de algoritmos es crucial para los algoritmos evolutivos de optimización multiobjetivo restringidos (CMOEA).
    • Las actuales CMOEA asistidas por aprendizaje se basan en técnicas manuales, a menudo subóptimas, diseñadas por expertos.
    • Los métodos existentes carecen de versatilidad y adaptabilidad en paisajes de optimización dinámica.

    Objetivo del estudio:

    • Desarrollar un método de configuración automatizado versátil y eficaz para los CMOEA.
    • Aprovechar el aprendizaje por refuerzo profundo (DRL, por sus siglas en inglés) para la autoadaptación de los parámetros y operadores de la CMOEA.
    • Mejorar el rendimiento y la adaptabilidad de las OCMEA mediante el diseño automatizado.

    Principales métodos:

    • Transformó la configuración en línea de CMOEA en una determinación de parámetros discreta y continua.
    • Aprendizaje de refuerzo profundo aplicado (DRL), específicamente Actor-Critic y Q-learning profundo, para la configuración automatizada.
    • Desarrolló un nuevo CMOEA que incorpora el algoritmo evolutivo configurado automáticamente (EA).

    Principales resultados:

    • El CMOEA configurado con DRL demostró mejoras significativas en el rendimiento con respecto a 11 métodos de última generación.
    • Los experimentos con puntos de referencia desafiantes y los problemas del mundo real validaron la superioridad del método propuesto.
    • La configuración automatizada mostró una mayor versatilidad y eficacia en comparación con los enfoques artesanales.

    Conclusiones:

    • La configuración automatizada utilizando DRL ofrece una dirección prometedora para avanzar en la optimización multiobjetivo evolutiva.
    • La capacidad de autoaprendizaje del CMOEA configurado con DRL mejora su capacidad de adaptación y rendimiento.
    • Este trabajo establece un nuevo paradigma para el diseño de CMOEA versátiles y de alto rendimiento.