Adaptive and Robust Multi-Agent Systems

Abstract

We applied tools based on quantitative genetic theory in order to improve Evolutionary Algorithms for use with team learning tasks. We reviewed the quantitative genetics literature more widely, and developed a theoretical analysis applying genetic theory to the team learning problem. We then constructed and analyzed a neural network structure and new genetic operators which more effectively divide the feature space for the Evolutionary Algorithm. We performed experiments and discovered that the new operators and structure produced more parsimonious results. We plan to publish these results in an upcoming conference following more rigorous experiments.

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Document Details

Document Type
Technical Report
Publication Date
Jul 03, 2008
Accession Number
ADA483761

Entities

People

  • Jeffrey K. Bassett
  • Sean Luke

Organizations

  • George Mason University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Biological Sciences
  • Biology
  • Department Of Defense
  • Equations
  • Evolutionary Algorithms
  • Genetics
  • Information Operations
  • Learning
  • Literature
  • Mathematics
  • Military Research
  • Multiagent Systems
  • Neural Networks
  • Quantitative Genetics

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.
  • Technical Research and Report Writing.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Biotechnology
  • Space