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Related Concept Videos

Genetic Drift03:33

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.Life is not fair. A deer grazing contentedly in a field can have her meal cut tragically short by a bolt of lightning. If the doomed doe is one of only three in the population, 1/3 of the population’s gene pool is lost. Random events like this can...
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).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...
Gene Flow02:39

Gene Flow

Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
What is Population Genetics?01:25

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.While some alleles of a given gene might be observed commonly, other variants...
Limits to Natural Selection01:38

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.For one, natural selection can only act upon existing genetic variation. Hypothetically, redtusks may enhance elephant survival by deterring ivory-seeking poachers. However, if there are no gene variants—or alleles—for redtusks, natural selection cannot increase the prevalence of...

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Related Experiment Video

Updated: Jun 30, 2026

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

Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.

Shengxiang Yang1

  • 1Department of Computer Science, University of Leicester, University Road, Leicester LE1 7RH, UK. s.yang@mcs.le.ac.uk

Evolutionary Computation
|September 25, 2008
PubMed
Summary

New hybrid schemes for genetic algorithms improve performance in dynamic optimization problems. Memory-based and elitism-based immigrants efficiently adapt algorithms to changing environments by combining diversity and historical information.

Related Experiment Videos

Last Updated: Jun 30, 2026

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

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization

Background:

  • Dynamic optimization problems present challenges for traditional genetic algorithms.
  • Existing approaches like random immigrants and memory schemes have limitations in adapting to environmental changes.
  • Maintaining population diversity and rapid adaptation are key to solving dynamic problems.

Purpose of the Study:

  • To investigate novel hybrid schemes for genetic algorithms in dynamic environments.
  • To introduce memory-based immigrants and elitism-based immigrants schemes.
  • To evaluate the effectiveness of these new schemes against existing methods.

Main Methods:

  • Developed hybrid genetic algorithm schemes: memory-based immigrants and elitism-based immigrants.
  • Implemented a strategy where immigrants are generated by mutating the best individual from memory or the elite.
  • Conducted experiments on systematically constructed dynamic problems to compare performance.
  • Performed sensitivity analysis on key parameters.

Main Results:

  • Memory-based immigrants and elitism-based immigrants schemes significantly improve genetic algorithm performance in dynamic environments.
  • These hybrid schemes efficiently balance population diversity with adaptation to environmental changes.
  • Experimental results demonstrate superior performance compared to traditional memory, random immigrants, and hybrid memory/multi-population schemes.

Conclusions:

  • Hybrid memory-based and elitism-based immigrants schemes offer an efficient approach to enhance genetic algorithms for dynamic optimization.
  • These methods effectively address the challenges of maintaining diversity and adapting to environmental shifts.
  • The proposed schemes represent a valuable advancement in the field of genetic algorithms for dynamic environments.