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Mutation, Gene Flow, and Genetic Drift

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).

Genetic Drift

Genetic Drift

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.

Hardy-Weinberg Principle

Hardy-Weinberg Principle

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.

Genetics of Speciation

Genetics of Speciation

Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.

Gene Flow

Gene Flow

Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.

Limits to Natural Selection

Limits to Natural Selection

Organisms that are well-adapted to their environment are more likely to survive and reproduce. However, natural selection does not lead to perfectly adapted organisms. Several factors constrain natural selection.

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Related Experiment Video

Updated: Jan 15, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Population-based variance-reduced evolution over stochastic landscapes.

Zelin Pei¹, Xiaoyu He², Yi Pan¹

¹School of Software Engineering, Sun Yat-Sen University, Guangzhou, 510006, China.

Scientific Reports

|October 7, 2025

Summary

This summary is machine-generated.

We introduce Population-based Variance-Reduced Evolution (PVRE), a novel optimization method that reduces noise in both solution and data spaces. PVRE achieves state-of-the-art convergence for zeroth-order optimization problems.

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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

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Published on: July 4, 2007

Area of Science:

Optimization
Machine Learning
Evolutionary Computation

Background:

Black-box stochastic optimization requires sampling in solution and data spaces.
Traditional methods struggle with convergence when solution space noise is high.

Purpose of the Study:

To present a novel zeroth-order optimization method, Population-based Variance-Reduced Evolution (PVRE).
To simultaneously mitigate noise in both solution and data spaces for improved convergence.

Main Methods:

PVRE employs a normalized-momentum mechanism for data sampling noise reduction.
Incorporates a population-based gradient estimation scheme to reduce solution space noise.

Main Results:

PVRE demonstrates theoretical convergence properties and evolutionary algorithm adaptability.
Achieves the best-known function evaluation complexity for finding ε-accurate first-order optimal solutions.

Conclusions:

PVRE offers an effective approach for black-box stochastic optimization.
Validated through experiments on benchmark problems and adversarial attacks on neural image classifiers.