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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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Related Experiment Video

Updated: Oct 15, 2025

Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria
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International Trade Path with Multi-Polarization based on Multidirectional Mutation Genetic Algorithm Enabled Neural

Qing Zhang1, Choo Wei Chong1, Abdul Rashid Abdullah1

  • 1School of Business and Economics University Putra Malaysia, Serdang, Selangor Darul Ehsan 43400, Malaysia.

Computational Intelligence and Neuroscience
|October 25, 2021
PubMed
Summary
This summary is machine-generated.

Optimizing international trade logistics is crucial for economic growth. This study introduces a novel neural network algorithm for enhanced genetic optimization, improving trade transportation path planning and efficiency.

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Area of Science:

  • Logistics and Supply Chain Management
  • Artificial Intelligence in Economics
  • Computational Optimization

Background:

  • International trade development lags behind economic growth, impacting national economies.
  • Efficient trade vehicle scheduling and transportation path planning are vital for business competitiveness.
  • Current optimization methods face limitations in adaptability and efficiency.

Purpose of the Study:

  • To address the challenges in international trade logistics by optimizing transportation paths.
  • To enhance enterprise services, reduce costs, and increase benefits through improved planning.
  • To develop a more effective optimization algorithm for real-world trade scenarios.

Main Methods:

  • Development of a neural network algorithm incorporating genetic optimization with multiple mutations.
  • Integration of mixed coding methods to overcome traditional genetic algorithm population distribution issues.
  • Introduction of a large-mutation small-range search population to boost local search capabilities.
  • Utilization of the Baidu Map programming interface for practical application.

Main Results:

  • The proposed algorithm effectively optimizes international trade paths under actual road conditions.
  • Demonstrated improvement in the efficiency of actual trade operations.
  • Overcame limitations of traditional genetic algorithms, enhancing search and adaptability.

Conclusions:

  • The novel neural network-based genetic optimization algorithm significantly improves international trade transportation path planning.
  • This approach offers a practical solution for enhancing efficiency and competitiveness in international trade.
  • The method provides a valuable tool for businesses seeking to optimize their logistics operations.