Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Life Histories01:29

Life Histories

17.8K
Overview
17.8K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.7K
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...
5.7K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

58.3K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
58.3K
Energy Budgets00:51

Energy Budgets

9.2K
Organisms must balance energy intake with the energy required for growth, maintenance and reproduction. These trade-offs result in a variety of survivorship and reproductive strategies, including semelparity and iteroparity. Semelparous species, like annual plants, have only one reproductive episode in their lifetimes and consequently have short lifespans. Iteroparous species, by contrast, have many reproductive events during their lifetimes but have relatively few offspring. These two...
9.2K
Synthetic Biology02:55

Synthetic Biology

4.7K
Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
4.7K
Types of Genetic Transfer Between Organisms02:18

Types of Genetic Transfer Between Organisms

27.2K
Genetic transfer occurs when genetic information is passed from one organism to another. It occurs via two mechanisms: vertical gene transfer and horizontal gene transfer. Vertical gene transfer occurs when genetic information is transferred from one generation to the next, which happens much more frequently than horizontal gene transfer. Both sexual and asexual reproduction are forms of vertical gene transfer, where one or more organisms pass some or all of their genome onto their progeny.
27.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Retraction Note: Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction.

Soft computing·2025
Same author

Chatbots and messaging platforms in the classroom: An analysis from the teacher's perspective.

Education and information technologies·2023
Same author

Architecture Optimization of a Non-Linear Autoregressive Neural Networks for Mackey-Glass Time Series Prediction Using Discrete Mycorrhiza Optimization Algorithm.

Micromachines·2023
Same author

Interval Type-3 Fuzzy Adaptation of the Bee Colony Optimization Algorithm for Optimal Fuzzy Control of an Autonomous Mobile Robot.

Micromachines·2022
Same author

A Novel Method for a COVID-19 Classification of Countries Based on an Intelligent Fuzzy Fractal Approach.

Healthcare (Basel, Switzerland)·2021
Same author

Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction.

Soft computing·2021

相关实验视频

Updated: Jun 15, 2025

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
11:58

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

9.4K

扩展遗传算法与生物生命周期动力学

J C Felix-Saul1, Mario García-Valdez1, Juan J Merelo Guervós2

  • 1Division of Graduate Studies and Research, Tijuana Institute of Technology, Tecnológico Nacional de México (TecNM), Tijuana 22414, Mexico.

Biomimetics (Basel, Switzerland)
|August 28, 2024
PubMed
概括

这项研究通过建模生物生命周期,提高多样性和适应性来增强遗传算法 (GA). 新方法在基准问题上表现优于传统的GA和其他算法.

关键词:
生物启发的算法是生物启发的算法.计算优化优化计算优化进化算法是指进化的算法.遗传算法 遗传算法

更多相关视频

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER
07:26

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

Published on: May 19, 2019

11.9K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

931

相关实验视频

Last Updated: Jun 15, 2025

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
11:58

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

9.4K
Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER
07:26

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

Published on: May 19, 2019

11.9K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

931

科学领域:

  • 进化计算是一种进化计算.
  • 人工智能的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 遗传算法 (GA) 经常在维持人口多样性和适应性方面扎.
  • 传统的GA可以过早收,失去最佳解决方案.
  • 生物生命周期为动态人口管理提供了一个强大的模型.

研究的目的:

  • 通过整合灵感来自生物生命周期的动态模型来增强遗传算法 (GA).
  • 为了应对维护多样性和适应能力的挑战在GA.
  • 提高GA在解决复杂计算问题的性能.

主要方法:

  • 将出生,生长,繁殖和死亡的阶段纳入GA框架.
  • 实现对个体生命周期阶段的异步执行.
  • 使用稳定状态进化方法来保持高质量的解决方案和多样性.

主要成果:

  • 与传统的GA相比,拟议的GA扩展显示出更高的性能.
  • 增强的GA在基准问题上取得了与粒子集群优化 (PSO) 和EvoSpace相比的或更好的结果.
  • 在融合速度和解决方案质量方面观察到显著的改进.

结论:

  • 整合生物生命周期动态增强了遗传算法的稳定性和效率.
  • 异步,稳定状态演变模型有效平衡解决方案质量和多样性.
  • 这项研究为推进进化计算技术提供了一个有前途的方向.