A Study of Crossover Operators in Genetic Programming.

Abstract

Holland's analysis of the sources of power of genetic algorithms has served as guidance for the applications of genetic algorithms for more than 15 years. The technique of applying a recombination operator (crossover) to a population of individuals is a key to that power. Neverless, there have been a number of contradictory results concerning crossover operators with respect to overall performance. Recently, for example, genetic algorithms were used to design neural network modules and their control circuits. In these studies, a genetic algorithm without crossover outperformed a genetic algorithm with crossover. This report re-examines these studies, and concludes that the results were caused by a small population size. New results are presented that illustrate the effectiveness of crossover when the population size is larger. From a performance view, the results indicate that better neural networks can be evolved in a shorter time if the genetic algorithm uses crossover. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1991
Accession Number
ADA294071

Entities

People

  • Vic Anand
  • William M. Spears

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Angular Acceleration
  • Compressed Sensing
  • Computations
  • Computer Programming
  • Demographic Cohorts
  • Environment
  • Genetic Algorithms
  • Information Processing
  • Information Science
  • Joints (Anatomy)
  • Military Research
  • Mutations
  • Neural Networks
  • Sampling
  • Statistics
  • Urban Areas

Readers

  • Molecular Genetics
  • Neural Network Machine Learning.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Biotechnology