Evolving Neural Network Connectivity

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 connections are incorporated into an objective function so that network parameter optimization is done with respect to network complexity as well as mean pattern error. Experimental results are shown for simple binary mapping problems. Neutral networks, Evolutionary programming, Signal detection.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1993
Accession Number
ADA273134

Entities

People

  • D. Waagen
  • J. R. Mcdonnell

Organizations

  • Naval Command, Control and Ocean Surveillance Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Coefficients
  • Computational Complexity
  • Computer Programming
  • Computing System Architectures
  • Genetic Algorithms
  • Machine Learning
  • Network Architecture
  • Neural Networks
  • Optimization
  • Personal Information Managers
  • 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 - Machine Learning Algorithms
  • AI & ML - Neural Networks