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Related Concept Videos

Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

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).
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Evolution of New Traits in Microbes01:24

Evolution of New Traits in Microbes

Microorganisms evolve rapidly due to their large population sizes and short generation times, often exhibiting measurable changes within days under laboratory conditions. Natural selection acts on standing genetic variation, enabling the retention and amplification of beneficial traits that confer fitness advantages in changing environments.Adaptive Pigment Regulation in RhodobacterIn Rhodobacter, a genus of purple non-sulfur bacteria, light-harvesting pigments such as bacteriochlorophyll and...
Mutations01:39

Mutations

Overview
Mutations01:35

Mutations

Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
Chromosomal Alterations Are Large-Scale Mutations
While point mutations are changes in a single nucleotide in...

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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

Differential evolution with ranking-based mutation operators.

Wenyin Gong, Zhihua Cai

    IEEE Transactions on Cybernetics
    |June 13, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces ranking-based mutation operators for differential evolution (DE) algorithms. By prioritizing higher-ranked individuals, this new approach enhances the performance of DE and its advanced variants.

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    Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli

    Published on: March 16, 2011

    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Evolutionary Computation

    Background:

    • Differential evolution (DE) is a powerful global numerical optimization algorithm.
    • The core of DE relies on a differential mutation operator, typically using randomly selected parents.
    • Biological systems often leverage information from successful individuals to guide others.

    Purpose of the Study:

    • To introduce novel ranking-based mutation operators for differential evolution (DE).
    • To enhance the selection process of parents in DE's mutation operator by considering their population rank.
    • To evaluate the performance improvement of DE algorithms using these new operators.

    Main Methods:

    • Proposed ranking-based mutation operators where parents are proportionally selected based on their current population ranking.
    • Compared the proposed operators against the jDE algorithm, a competitive DE variant with self-adaptive parameters.
    • Integrated the ranking-based mutation operators into other advanced DE variants to assess their broader impact.

    Main Results:

    • The proposed ranking-based mutation operators significantly enhance the performance of the original DE algorithm.
    • Integration of these operators also improves the performance of advanced DE variants.
    • The ranking-based selection strategy proved effective in boosting optimization capabilities.

    Conclusions:

    • Ranking-based mutation operators offer a valuable enhancement to differential evolution algorithms.
    • This approach effectively leverages information from high-performing individuals to guide the optimization process.
    • The proposed method demonstrates improved performance across various DE algorithm implementations.