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Related Concept Videos

Law of Independent Assortment02:03

Law of Independent Assortment

While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
Law of Independent Assortment02:03

Law of Independent Assortment

While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

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Within the human body, a complex and detailed system of trillions of cells works in unison to sustain life. Each cell houses a nucleus, which contains 46 chromosomes divided into 23 pairs. Chromosomes are highly coiled structures made of the genetic material DNA. These chromosomes are essential carriers of genetic information, with half inherited from the mother through her egg and the other half from the father's sperm, combining to create the unique genetic makeup of an individual.
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Generating Transgenic Plants with Single-copy Insertions Using BIBAC-GW Binary Vector
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Published on: March 28, 2018

Representation invariant genetic operators.

Jonathan E Rowe1, Michael D Vose, Alden H Wright

  • 1School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK. J.E.Rowe@cs.bham.ac.uk

Evolutionary Computation
|June 30, 2010
PubMed
Summary
This summary is machine-generated.

Genetic algorithms achieve representation invariance when their operators are independent of the encoding used. This study mathematically defines invariance and characterizes operators that ensure consistent performance across different representations.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Genetic algorithms (GAs) are optimization techniques inspired by natural selection.
  • The performance of GAs can be sensitive to the chosen representation of solutions.
  • Representation invariance ensures algorithm robustness across different encodings.

Purpose of the Study:

  • To mathematically formalize the concept of representation invariance in genetic algorithms.
  • To identify and characterize genetic operators that exhibit representation invariance.
  • To develop representation-independent algorithms for crossover and mutation.

Main Methods:

  • Formal mathematical definition of representation invariance using group theory.
  • Analysis of genetic operators (crossover and mutation) for invariance properties.
  • Development of high-level algorithms for invariant operators.

Main Results:

  • Established that representations form a group acting on the search space.
  • Defined invariant genetic operators as those commuting with the group action.
  • Provided a complete characterization of invariant operators for transitively acting groups.

Conclusions:

  • Representation invariance is a crucial property for robust genetic algorithms.
  • Invariant operators ensure consistent performance regardless of solution encoding.
  • The developed framework and algorithms enable the design of more general and reliable GAs.