Evolving Neural Network Pattern Classifiers

Abstract

This work investigates the application of evolutionary programming for automatically configuring neural network architectures for pattern classification tasks. The evolutionary programming search procedure implements a parallel nonlinear regression technique and represents a powerful method for evaluating a multitude of neural network model hypotheses. The evolutionary programming search is augmented with the Solis & Wets random optimization method thereby maintaining the integrity of the stochastic search while taking into account empirical information about the response surface. A network architecture is proposed which is motivated by the structures generated in projection pursuit regression and the cascade-correlation learning architecture. Results are given for the 3-bit parity, normally distributed data, and the T-C classifier problems. Evolutionary programming, Neural networks, Signal detection.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1994
Accession Number
ADA281181

Entities

People

  • D. E. Waagen
  • J. R. Mcdonnell
  • W. C. Page

Organizations

  • Naval Command, Control and Ocean Surveillance Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Classification
  • Computer Programming
  • Computing System Architectures
  • Costs
  • Data Sets
  • Genetic Algorithms
  • Learning
  • Machine Learning
  • Network Architecture
  • Neural Networks
  • Optimization
  • Signal Detection
  • Signal Processing
  • Training

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks