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Related Concept Videos

Frequency-dependent Selection01:21

Frequency-dependent Selection

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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.
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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.
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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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...
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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.
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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection.

Mohammed Abdelrazek1, Mohamed Abd Elaziz2,3,4,5, A H El-Baz6

  • 1Department of Mathematics, Faculty of Science, Damietta University, New Damietta, 34517, Egypt.

Scientific Reports
|January 6, 2024
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Summary
This summary is machine-generated.

A new Chaotic Dwarf Mongoose Optimization Algorithm (CDMO) enhances feature selection by incorporating chaotic maps. This improved method achieves superior classification accuracy and performance compared to existing algorithms.

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Area of Science:

  • Machine Learning
  • Swarm Intelligence
  • Optimization Algorithms

Background:

  • Feature selection is crucial for improving classification accuracy in machine learning.
  • Swarm intelligence algorithms, like the Dwarf Mongoose Optimization Algorithm (DMO), offer novel approaches to optimization.
  • Existing meta-heuristic techniques have limitations in convergence speed and effectiveness.

Purpose of the Study:

  • To propose a modified Dwarf Mongoose Optimization Algorithm (DMO) for enhanced feature selection.
  • To improve the convergence speed and effectiveness of the DMO algorithm.
  • To evaluate the performance of the proposed Chaotic DMO (CDMO) against other optimization techniques.

Main Methods:

  • A wrapper-based feature selection model named Chaotic DMO (CDMO) was developed.
  • Ten chaotic maps were integrated into the DMO algorithm to modify its movement patterns.
  • The CDMO was tested on ten UCI datasets and benchmark functions, comparing it against DMO and other meta-heuristic algorithms (ACO, WOA, ARO, HHO, EO, RTHS, RSGW, SSAPSO, BGA, ASGW, PSO).

Main Results:

  • The CDMO demonstrated superior performance in feature selection compared to the original DMO and other methods.
  • High classification accuracy (91.9-100%), sensitivity (77.6-100%), precision (91.8-96.08%), specificity (91.6-100%), and F-Score (90-100%) were achieved across ten UCI datasets.
  • The CDMO also showed effectiveness when assessed against CEC'2022 benchmark functions.

Conclusions:

  • The Chaotic DMO (CDMO) is an effective and efficient algorithm for feature selection.
  • The integration of chaotic maps significantly enhances the performance of the DMO algorithm.
  • CDMO offers a promising approach for achieving high classification accuracy in machine learning tasks.