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What is Natural Selection?

What is Natural Selection?

Natural selection is an evolutionary process in which individuals with survival-promoting traits reproduce at higher rates. These favorable traits become more common within a population or species. Naturally selected traits initially arise via random genetic mutations. In order for selection to occur, there must be variation within a population, the trait controlling the variation must be heritable, and there must be an evolutionary advantage for variation in the trait.

Antibiotic Selection

Antibiotic Selection

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.

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.

Natural Selection and Adaptation

Natural Selection and Adaptation

Natural selection, a fundamental concept in evolutionary biology, is the mechanism by which evolution is driven, favoring organisms that are best adapted to their environments. This process enhances their chances of survival and reproduction. Adaptation, a key outcome of this process, involves genetic modifications that optimize an organism's functionality under specific environmental challenges, such as extreme cold or thinner air at high altitudes.
Beyond physical adaptations,...

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Hypervolume Subset Selection with Small Subsets.

Benoît Groz¹, Silviu Maniu²

¹LRI, Université Paris-Sud, Université Paris-Saclay, Orsay, France benoit.groz@lri.fr.

Evolutionary Computation

|October 27, 2018

Summary

This summary is machine-generated.

This study introduces efficient algorithms for the hypervolume subset selection problem (HSSP) in higher dimensions. The new methods improve approximation quality for multiobjective evolutionary algorithms, especially when subset size is small.

Keywords:

Hypervolume subset selection hypervolume contributions minimal triples in weighted hypergraphs.

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Area of Science:

Multiobjective Optimization
Computational Geometry
Evolutionary Algorithms

Background:

The hypervolume subset selection problem (HSSP) is crucial for approximating complex datasets in multiobjective optimization.
Existing efficient algorithms for HSSP are limited to low dimensions (2D).
Higher-dimensional HSSP often relies on computationally expensive exhaustive subset enumeration.

Purpose of the Study:

To develop efficient algorithms for the hypervolume subset selection problem (HSSP) in dimensions beyond two.
To improve the approximation quality of subsets for multiobjective evolutionary algorithms.
To address the computational challenges of HSSP in higher dimensions.

Main Methods:

Developing novel algorithms for HSSP in 3D, particularly when the subset size or the number of points is small.
Extending these techniques to arbitrary dimensions for small subset sizes.
Focusing on efficient selection rather than solely on hypervolume computation.

Main Results:

Proposed efficient algorithms for HSSP in 3D, outperforming existing methods when specific parameters are small.
Demonstrated the scalability of the developed techniques to arbitrary dimensions for small subset sizes.
Provided a more computationally feasible approach to HSSP in higher dimensions.

Conclusions:

The new algorithms offer significant improvements for HSSP in higher dimensions, especially for small subset sizes.
These advancements enhance the practical application of multiobjective evolutionary algorithms.
The research opens avenues for further exploration of efficient HSSP algorithms in complex, high-dimensional spaces.