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相关概念视频

Genetic Drift03:33

Genetic Drift

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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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Mutation, Gene Flow, and Genetic Drift01:09

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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).
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Gene Flow02:39

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Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.
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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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What is Population Genetics?01:25

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A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
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The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
Exon shuffling follows “splice frame rules.” Each exon...
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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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无参数的基因池 最佳混合 进化算法

Arkadiy Dushatskiy1, Marco Virgolin2, Anton Bouter3

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概括
此摘要是机器生成的。

这项研究增强了基因库最佳混合进化算法 (GOMEA),并引入了CGOMEA以实现更好的优化. 这些改进的进化算法 (EA) 通过有效检测和利用可变依赖关系,显著优于现有方法.

关键词:
基于模型的进化算法.估计分布的算法估计分布的算法.遗传算法 遗传算法链接学习的学习链接.最佳的混合最佳的混合.

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科学领域:

  • 人工智能的人工智能
  • 计算优化计算优化
  • 进化计算是一种进化计算.

背景情况:

  • 进化算法 (EAs) 是优化的强大工具,但它们的性能往往取决于有效地检测和利用可变依赖关系 (链接).
  • 基因库最佳混合进化算法 (GOMEA) 旨在解决链接学习问题,但需要进一步改进以获得更广泛的适用性和更好的性能.
  • 现有的链接意识EA,如DSMGA-II,提供竞争性的解决方案,但可能无法完全捕捉复杂的依赖结构.

研究的目的:

  • 通过大规模的设计空间搜索来优化GOMEA的增强版本.
  • 引入一种新型变体,CGOMEA,通过结合条件依赖过来改善链接利用.
  • 评估与DSMGA-II相比增强的GOMEA和CGOMEA在挑战黑子优化问题上的表现,并通过自动人口管理调查无参数操作.

主要方法:

  • 关于需要发现联系的九个基准黑子问题的GOMEA和CGOMEA的广泛实证评估.
  • 与领先的链接意识EA,DSMGA-II进行比较,以评估相对性能.
  • 调查自动人口管理方案,以提高GOMEA和CGOMEA的可用性和稳定性,旨在实现无参数操作.

主要成果:

  • 在大多数测试问题上,优化的GOMEA和新型CGOMEA在大多数测试问题上显示出比原来的GOMEA和DSMGA-II显著的性能改进.
  • 通过条件依赖过增强的CGOMEA的基于链接的变化,证明特别有效.
  • 自动人口管理方案使GOMEA和CGOMEA能够在减少参数调节的情况下实现竞争性结果,从而提高其实际适用性.

结论:

  • 增强的GOMEA和CGOMEA代表了复杂的优化问题的链接意识进化计算的最新状态.
  • 利用条件依赖提供了一个有希望的途径,以进一步改善EA的链接学习.
  • 通过自动化人口管理开发无参数EA,提高了实践人员的可访问性和可靠性.