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Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

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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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Genetic Variation01:25

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
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Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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Genetic Drift03:33

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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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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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Gene Duplication and Divergence02:37

Gene Duplication and Divergence

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The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are...
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Related Experiment Video

Updated: Aug 15, 2025

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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A Method of Merging Maps for MUAVs Based on an Improved Genetic Algorithm.

Quansheng Sun1, Tianjun Liao2, Haibo Du1

  • 1School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary

This study presents a novel genetic algorithm for merging environmental maps from multiple unmanned aerial vehicles (UAVs) without a common coordinate system. The directed crossover multidimensional perturbation variational genetic algorithm (DCPGA) efficiently finds optimal map overlaps for improved exploration.

Keywords:
LiDARMUAVsgenetic algorithmmap mergingoptimization problem

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Area of Science:

  • Robotics
  • Computer Vision
  • Geographic Information Systems

Background:

  • Distributed multi-UAV exploration relies on efficient map merging.
  • Merging maps is challenging without a common reference coordinate system or relative UAV positioning.

Purpose of the Study:

  • To address the raster map-merging problem for distributed multi-UAV systems.
  • To propose a novel algorithm for merging maps lacking common spatial references.

Main Methods:

  • A directed crossover multidimensional perturbation variational genetic algorithm (DCPGA) was developed.
  • The algorithm uses map region dissimilarity as a fitness function.
  • Chromosomes represent rotation and translation transformations; genes encode coordinates.

Main Results:

  • The DCPGA demonstrated rapid convergence.
  • The algorithm exhibited strong global search capabilities for optimal overlap detection.
  • Successful merging of raster maps was achieved.

Conclusions:

  • The proposed DCPGA effectively solves the raster map-merging problem in challenging conditions.
  • This method enhances the efficiency of distributed multi-UAV exploration by enabling seamless map integration.