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Related Concept Videos

Pareto Chart00:52

Pareto Chart

A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Cluster Sampling Method01:20

Cluster Sampling Method

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Decision Making: P-value Method01:09

Decision Making: P-value Method

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Classification of Systems-I01:26

Classification of Systems-I

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Classification of Systems-II

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Related Experiment Video

Updated: Jun 8, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Classification as clustering: a Pareto cooperative-competitive GP approach.

Andrew R McIntyre1, Malcolm I Heywood

  • 1Faculty of Computer Science, Dalhousie University, Halifax, B3H 1W5, Canada. armcnty@cs.dal.ca

Evolutionary Computation
|October 1, 2010
PubMed
Summary

This study introduces a novel framework for evolving teams of genetic programming individuals without a fixed team size. This approach enhances classification performance and model simplicity, particularly for large datasets.

Related Experiment Videos

Last Updated: Jun 8, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Evolutionary Computation

Background:

  • Population-based algorithms like genetic programming naturally support task decomposition among multiple individuals.
  • Existing methods often require pre-specifying the number of cooperating individuals in a team.

Purpose of the Study:

  • To present a framework for evolving teams of cooperating individuals without pre-specifying team size.
  • To enable individuals to learn unique subsets of a classification task.

Main Methods:

  • Evolving individuals that map to outcome distributions, which parameterize local membership functions after clustering.
  • Utilizing evolutionary multiobjective optimization (EMO) for accurate and non-overlapping behaviors.
  • Employing Pareto competitive coevolution for scalability with large, unbalanced datasets.

Main Results:

  • The proposed framework balances classification performance and model complexity.
  • Effectiveness is demonstrated across 12 UCI datasets with varying instance counts.
  • Performance is competitive with nonlinear Support Vector Machine (SVM) classifiers, especially on large datasets.

Conclusions:

  • The coevolutionary multiobjective genetic programming framework offers an effective approach to team evolution for classification.
  • The method successfully handles task decomposition and scales to large datasets.
  • This approach provides a flexible alternative to fixed-size team structures in evolutionary computation.