Rapid Solutions to Hard Problems Using Fast Messy Genetic Algorithms

Abstract

This project developed and applied a type of non-traditional genetic algorithm called a fast messy genetic algorithm (fmGA). Critical bounding theory and computational experiments show that fmGAs converge to high quality solutions with high probability in times that grow no faster than a sub quadratic function of the number of decision variables. These results have important ramifications for the design and operation of the next generation of Air Force systems.

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Document Details

Document Type
Technical Report
Publication Date
Feb 25, 1997
Accession Number
ADA332334

Entities

People

  • David E. Goldberg

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Artificial Intelligence
  • Computational Science
  • Computations
  • Computer Science
  • Computers
  • Data Mining
  • Electromagnetic Scattering
  • Engineering
  • Gene Expression
  • Genetic Algorithms
  • Image Recognition
  • Machine Learning
  • Manufacturing
  • Neural Networks
  • Optimization

Fields of Study

  • Computer science

Readers

  • Maritime Combat Support and Expeditionary Logistics.
  • Operations Research

Technology Areas

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