From Theory to Air Force Practice: Applications and Non-Binary Extensions of Probabilistic Model-Building Genetic Algorithms

Abstract

These goals have been substantially accomplished. Moreover, new findings led to important discoveries that were unanticipated at the beginning of the project. The report starts with a retrospective examination of project accomplishment. 1) Develop, implement, and enhance probabilistic model-building GAs for non-binary codes. 2) Extend existing facet wise models to non-binary codes. 3) Extend bounding test functions to non-binary code. 4) Extend the proposed non-binary algorithms to hierarchically difficult problems. 5) Apply the developed algorithms to two problems of Air Force interest.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 31, 2006
Accession Number
ADA463557

Entities

People

  • David E. Goldberg

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Computational Science
  • Computations
  • Computer Programming
  • Data Mining
  • Evolutionary Algorithms
  • Genetic Algorithms
  • Heuristic Methods
  • Information Processing
  • Information Science
  • Machine Learning
  • Models
  • Multiscale Modeling
  • Probabilistic Models
  • Supervised Machine Learning
  • Systems Engineering

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Speech Processing/Speech Recognition.
  • Systems Analysis and Design

Technology Areas

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