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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light bulb,...
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

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Updated: Jul 10, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Comparison-based algorithms are robust and randomized algorithms are anytime.

Sylvain Gelly1, Sylvie Ruette, Olivier Teytaud

  • 1Equipe TAO (INRIA Futurs), LRI, UMR 8623 (CNRS - Université Paris-Sud), bat. 490 Université Paris-Sud 91405 Orsay Cedex, France. gelly@lri.fr

Evolutionary Computation
|November 21, 2007
PubMed
Summary

Randomized search heuristics offer good performance and are easy to implement. This study shows comparison-based methods enhance robustness, while offspring randomness improves anytime performance for optimization algorithms.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization

Background:

  • Randomized search heuristics, including evolutionary algorithms and simulated annealing, are widely used due to their practical implementation and effectiveness.
  • Theoretical analyses often concentrate on convergence rates, potentially overlooking other performance aspects.

Purpose of the Study:

  • To provide a mathematical analysis of randomized search heuristics employing comparison-based selection.
  • To investigate the impact of randomness in offspring selection on algorithm performance.
  • To develop and validate an improved algorithm based on these findings.

Main Methods:

  • Mathematical analysis of comparison-based selection mechanisms in randomized search.
  • Theoretical examination of the influence of randomness in offspring selection.
  • Development of a novel Estimation of Distribution Algorithm (EDA).

Main Results:

  • Comparison-based algorithms demonstrate superior performance according to specific robustness criteria.
  • Incorporating randomness in offspring selection enhances the anytime behavior of the algorithms.
  • The proposed EDA, integrating these principles, yielded successful experimental results.

Conclusions:

  • Randomized search heuristics with comparison-based selection offer significant robustness advantages.
  • Strategic introduction of randomness in offspring selection is key to improving anytime performance.
  • The novel EDA provides a practical and effective approach for optimization tasks.