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Genetic Screens

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

Types of Selection

Types of Selection

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...

Frequency-dependent Selection

Frequency-dependent Selection

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.Positive Frequency-Dependent SelectionIn positive...

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.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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Updated: Jun 22, 2026

A Method for Selecting Structure-switching Aptamers Applied to a Colorimetric Gold Nanoparticle Assay

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Threshold-selecting strategy for best possible ground state detection with genetic algorithms.

Jörg Lässig¹, Karl Heinz Hoffmann

¹Institut für Physik, Technische Universität Chemnitz, D-09107 Chemnitz, Germany. joerg.laessig@physik.tu-chemnitz.de

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|June 13, 2009

Summary

Genetic algorithms use threshold selecting for optimal individual selection in crossover. This strategy assigns uniform probabilities to low-energy individuals, improving performance in complex optimization problems.

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Area of Science:

Computational physics
Optimization algorithms
Heuristic methods

Background:

Genetic algorithms are widely used heuristics for finding low-energy states in complex systems like spin glasses and combinatorial optimization problems.
Selecting individuals for crossover is a critical step influencing genetic algorithm performance.
Various crossover selection strategies exist in the literature.

Purpose of the Study:

To determine the optimal probability distribution for selecting individuals for crossover in genetic algorithms.
To introduce and analyze a novel selection strategy called threshold selecting.

Main Methods:

The study employs basic arguments from Markov chains and linear optimization.
The analysis makes minimal assumptions about the underlying principles, ensuring broad applicability.
The proposed method involves sorting individuals by energy and applying a cutoff rank.

Main Results:

The optimal probability distribution for selection is a rectangular distribution over individuals sorted by energy.
This translates to assigning uniform probabilities to a subset of lowest-energy individuals and zero probability to others.
The threshold selecting strategy is proven to be optimal for a large class of quality measures.

Conclusions:

The threshold selecting strategy offers an optimal approach for individual selection in genetic algorithms.
This method enhances the efficiency of genetic algorithms in complex optimization tasks.
The findings are applicable to a wide range of genetic algorithms due to minimal underlying assumptions.