Evolving Neural Network Architecture

Abstract

This work investigates the application of a stochastic search technique, evolutionary programming, for developing self-organizing neural networks. The chosen stochastic search method is capable of simultaneously evolving both network architecture and weights. The number of synapses and neurons are incorporated into an objective function so that network parameter optimization is done with respect to computational costs as well as mean pattern error. Experiments are conducted using feedforward networks for simple binary mapping problems.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1993
Accession Number
ADA264802

Entities

People

  • Don Waagen
  • John R. Mcdonnell

Organizations

  • Naval Command, Control and Ocean Surveillance Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Computer Programming
  • Computing System Architectures
  • Data Science
  • Demographic Cohorts
  • Dilution
  • Dimensionality Reduction
  • Network Architecture
  • Neural Networks
  • Nodes
  • Ocean Surveillance
  • Optimization
  • Probability
  • Random Variables
  • Signal Processing
  • Training

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.

Technology Areas

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