Application of Genetic Algorithms to Function Decomposition in Pattern Theory.

Abstract

This report documents use of genetic algorithms for finding partitions which lead to optimal decomposition of boolean functions in the Ashenhurst-Curtis method of functional decomposition. This problem apparently grows exponentially as the number of input variables increase, but is useful to study since it has a myriad of potential applications in algorithm design, circuit design, image processing, data compression, logic minimization, and machine learning. The report presents some background on function decomposition, genetic algorithms and results of some experiments. Although use of genetic algorithms still result in exponential growth; they provide a much lower rate of growth.

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Document Details

Document Type
Technical Report
Publication Date
Jan 26, 1994
Accession Number
ADA327931

Entities

People

  • David A. Gadd
  • Mark Axtell
  • Michael J. Noviskey
  • Timothy D. Ross

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Compression
  • Data Compression
  • Decomposition
  • Genetic Algorithms
  • Heuristic Methods
  • Image Processing
  • Information Processing
  • Learning
  • Machine Learning
  • Mathematics
  • Signal Processing
  • Teamwork

Fields of Study

  • Computer science

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

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