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Related Concept Videos

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...
Types of Selection01:46

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...
What is Natural Selection?01:32

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.The Theory of Natural...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
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...

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Natural selection at work: an accelerated evolutionary computing approach to predictive model selection.

Olcay Akman1, Joshua W Hallam

  • 1Department of Mathematics, Illinois State University Normal, IL, USA.

Frontiers in Neuroscience
|July 28, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a genetic algorithm for predictive modeling, replacing stepwise regression. It utilizes the Information Complexity Measure (ICOMP) for model evaluation, enhancing predictive accuracy and efficiency.

Keywords:
diversificationgenetic algorithmsinformation complexity measurepopulation reductionstepwise regression

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Area of Science:

  • Computational statistics
  • Machine learning
  • Predictive modeling

Background:

  • Traditional stepwise regression methods can be suboptimal for complex datasets.
  • R-square, a common model fitness measure, may not fully capture model predictive power.
  • Genetic algorithms offer a robust alternative for model optimization.

Purpose of the Study:

  • To implement a genetic algorithm for predictive model building.
  • To utilize the Information Complexity Measure (ICOMP) as a superior alternative to R-square for model fitness assessment.
  • To enhance the efficiency of genetic algorithms through proposed modifications.

Main Methods:

  • Genetic algorithm implementation for predictive model construction.
  • Application of Information Complexity Measure (ICOMP) for evaluating model fit.
  • Development of modified genetic algorithms to improve computational efficiency.

Main Results:

  • The genetic algorithm approach provides a viable alternative to stepwise regression.
  • ICOMP demonstrates effectiveness as a model fitness criterion.
  • Proposed modifications enhance the overall efficiency of the genetic algorithm.

Conclusions:

  • Genetic algorithms coupled with ICOMP offer a powerful framework for predictive modeling.
  • The study highlights potential improvements in model building efficiency and accuracy.
  • This approach advances the field of statistical modeling and machine learning.