Adapting the Evaluation Space to Improve Global Learning,

Abstract

In domains where a stochastic process is involved in the evaluation of a candidate solution, multiple evaluations are necessary to obtain a good estimate of the performance of an individual. This work shows that biasing the sampling of that problem configuration space can lead to better performance of the structure being learned given the same amount of effort. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1991
Accession Number
ADA294069

Entities

People

  • Alan C. Schultz

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Environment
  • Genetic Algorithms
  • Image Registration
  • Learning
  • Maneuverability
  • Maneuvers
  • Military Research
  • Sampling
  • Side Effects
  • Statistical Sampling
  • Statistics
  • Test And Evaluation
  • Training
  • Two Dimensional
  • Weighting Functions

Fields of Study

  • Computer science

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Neural Network Machine Learning.
  • Software Engineering

Technology Areas

  • Space