Deception Considered Harmful.

Abstract

A central problem in the theory of genetic algorithms is the characterization of problems that are difficult for GAs to optimize. Many attempts to characterize such problems focus on the notion of deception, defined in terms of the static average fitness of competing schemas. This note argues this popular approach appears unlikely to yield a predictive theory for genetic algorithms. Instead, the characterization of hard problems must take into account the basic features of genetic algorithms, especially their dynamic, biased sampling strategy. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1993
Accession Number
ADA294072

Entities

People

  • John J. Grefenstette

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Competition
  • Complex Systems
  • Convergence
  • Deception
  • Demographic Cohorts
  • Dynamics
  • Electronic Mail
  • Genetic Algorithms
  • Mathematics
  • Mutations
  • Sampling
  • Standards
  • Transient Response Analysis
  • Walsh Functions

Readers

  • Computational Modeling and Simulation
  • Operations Research
  • Sensor Fusion and Tracking Systems.

Technology Areas

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