Biological Cybernetics

, Volume 63, Issue 2, pp 111–114 | Cite as

Comparing genetic operators with gaussian mutations in simulated evolutionary processes using linear systems

  • D. B. Fogel
  • J. W. Atmar


Evolutionary optimization has been proposed as a method to generate machine learning through automated discovery. Specific genetic operations (e.g. crossover and inversion) have been proposed to mutate the structure that encodes expressed behavior. The efficiency of these operations is evaluated in a series of experiments aimed at solving linear systems of equations. The results indicate that these genetic operators do not compare favorably with more simple random mutation.


Linear System Evolutionary Process Genetic Operator Evolutionary Optimization Random Mutation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag 1990

Authors and Affiliations

  • D. B. Fogel
    • 1
  • J. W. Atmar
    • 2
  1. 1.ORINCON CorporationSan DiegoUSA
  2. 2.AICS Research, Inc.University ParkUSA

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