Pattern Classification Using Genetic Algorithms: Determination of H

Abstract

A methodology based on the concept of variable string length GA (VGA) is developed for determining automatically the number of hyperplanes for modeling the class boundaries in GA classifier. The genetic operators and fitness function are newly defined to take care of the variability in chromosome length. It is proved that the said method is able to arrive at the optimal number of misclassifications after sufficiently large number of iterations, and will need minimal number of hyperplanes for this purpose. Experimental results on different artificial and real life data sets demonstrate that the classifier, using the concept of variable length chromosome, can automatically evolve an appropriate value of H, and also provide performance better than those of the fixed length version. Its comparison with another approach using VGA is provided.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 01, 1998
Accession Number
ADA358030

Entities

People

  • C. A. Murthy
  • S. K. Pal
  • Saumil Bandyopadhyay

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Boundaries
  • Chromosomes
  • Classification
  • Coding
  • Data Science
  • Data Sets
  • Decoding
  • Genetic Algorithms
  • Iterations
  • Machine Learning
  • Multivariate Analysis
  • Neural Networks
  • Pattern Recognition
  • United States Government

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Mathematical Modeling and Probability Theory.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Biotechnology