Determining Neural Network Connectivity Using Evolutionary Programming

Abstract

This work investigates the application of evolutionary programming, a stochastic search technique, for determining connectivity in feedforward neural networks. The method is capable of simultaneously evolving both the connection scheme and the network weights. The number of synapses are incorporated into an objective function so that network parameter optimization is done with respect to a connectivity cost as well as mean pattern error. Experimental results are shown 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
ADA266853

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
  • Annealing
  • Artificial Intelligence
  • Classification
  • Competition
  • Computer Programming
  • Computing System Architectures
  • Dimensionality Reduction
  • Machine Learning
  • Network Architecture
  • Neural Networks
  • Optimization
  • Probability
  • Random Variables
  • Signal Detection
  • Signal Processing
  • Training

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Operations Research

Technology Areas

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