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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

11.8K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.8K
Frequency-dependent Selection01:21

Frequency-dependent Selection

21.9K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
21.9K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
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...
45
Types of Selection01:46

Types of Selection

40.3K
Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
40.3K
Survival Tree01:19

Survival Tree

73
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
73
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K

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相关实验视频

Updated: Jun 15, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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调整强化学习参数用于集群选择以增强进化算法.

Nathan Villavicencio1, Michael N Groves2

  • 1Department of Mathematics, California State University Fullerton, Fullerton, California 92834, United States.

ACS engineering Au
|August 26, 2024
PubMed
概括

这项研究引入了一种新的方法,用于利用遗传算法优化分子结构搜索. 通过动态调整基于分子适应性的集群选择概率,该方法提高了效率,并在特定搜索中优于传统方法.

科学领域:

  • 计算化学的计算化学
  • 生物信息学是一种生物信息学.
  • 药物发现 药物发现 药物发现

背景情况:

  • 遗传算法 (GA) 广泛用于全球最小分子搜索,提供高效的能源景观的探索.
  • 在GA中群集群体可以提高效率并减少不稳定的结构,但群集之间的最佳选择策略仍然未得到充分探索.

研究的目的:

  • 开发和评估一种新的基因算法集群和动态选择策略,以平衡探索和开发.
  • 研究四个不同的参数 (MFavOvrAll-A,MFavClus-B,NoNewFavClus-C,Select-D) 对进化算法的性能的影响.

主要方法:

  • 一个基因算法被增强为一个人口集群系统和一个动态的基于概率的集群选择机制.
  • 定义了四个参数来调节集群选择,包括奖励卓越的表现和处罚表现差.
  • 使用高斯分布近似和网格搜索进行了参数优化.

主要成果:

  • 参数MFavOvrAll-A (总体最佳结构奖励) 和Select-D (选择比率罚款) 显示对绩效的影响明显大于MFavClus-B和NoNewFavClus-C.
  • 在MFavOvrAll-A和Select-D之间的平衡对于优化勘探-开发权衡至关重要,类似于强化学习.
  • 提出的基于强化学习的集群选择方法超过了用于类结构搜索的标准非集群遗传算法.

更多相关视频

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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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

Published on: December 9, 2012

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相关实验视频

Last Updated: Jun 15, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
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

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结论:

  • 在遗传算法中,动态的,取决于健康状况的集群选择在传统方法上提供了显著的改进.
  • 识别的关键参数为调整分子结构优化的进化算法提供了一个框架.
  • 这种方法有望加速药物发现和其他需要高效分子设计的应用.