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Related Concept Videos

Bootstrapping01:24

Bootstrapping

The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...
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Related Experiment Video

Updated: Jul 1, 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

Score-based resampling method for evolutionary algorithms.

Jonghwan Park1, Moongu Jeon, Witold Pedrycz

  • 1Division of Applied Robot Technology, Korea Institute ofIndustrial Technology, Ansan 426-791, Korea. johnpark@kitech.re.kr

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 12, 2008
PubMed
Summary
This summary is machine-generated.

A novel gene-handling method for evolutionary algorithms (EAs) improves optimization by addressing ineffective genes. This score-based resampling technique enhances solution quality and reduces uncertainty in complex systems.

Related Experiment Videos

Last Updated: Jul 1, 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

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Evolutionary algorithms (EAs) use nonanalytic optimization for complex systems.
  • Generic EAs face challenges with noneffective genes leading to local optima and reduced performance.
  • This can cause uncertainty and unbalanced system behavior.

Purpose of the Study:

  • To propose a new gene-handling method for evolutionary algorithms.
  • To address the issue of noneffective genes causing local optima and performance degradation.
  • To improve the reliability and quality of solutions in complex system optimization.

Main Methods:

  • Introduced a score-based resampling method for gene handling within EAs.
  • Developed a score function to evaluate genes based on their contribution.
  • Applied the method to handle genes at the allele level during evolutionary processes.

Main Results:

  • The proposed gene-handling method effectively addresses noneffective genes.
  • Empirical evaluation demonstrated improved optimization performance across various test functions.
  • The method alleviates the problem of solutions being trapped in local optima.

Conclusions:

  • The score-based resampling method enhances the effectiveness of evolutionary algorithms.
  • This approach leads to more robust and higher-quality solutions in complex optimization tasks.
  • The proposed method offers a valuable improvement for EA gene handling.