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Related Experiment Videos

Genetic algorithms for route discovery.

Erol Gelenbe1, Peixiang Liu, Jeremy Lainé

  • 1Department of Electrical and Electronic Engineering, Intelligent Systems and Networks (ISN), Imperial College, SW7 2BT London, UK. e.gelenbe@imperial.ac.uk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|December 26, 2006
PubMed
Summary

This study explores using genetic algorithms (GAs) to enhance network routing. GAs improve quality-of-service (QoS) under light to medium network loads but may degrade performance under heavy loads.

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Effective packet routing relies on dynamic or static path knowledge.
  • Quality-of-Service (QoS) routing protocols dynamically select paths based on user requirements.

Purpose of the Study:

  • To investigate the use of genetic algorithms (GAs) for enhancing path discovery in networks.
  • To extend the Cognitive Packet Network (CPN) protocol with GA-based path modification.

Main Methods:

  • Implemented a GA at source routers to modify and filter paths discovered by the CPN protocol.
  • GA combines previously discovered paths to create new source-to-destination paths.
  • GA operates in background mode to avoid overloading ingress routers.

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Main Results:

  • GA-enhanced routing improved QoS under light to medium network traffic loads.
  • Under high background traffic, GA routing showed diminished QoS compared to standard CPN routing.
  • GA decision-making relied on less timely state information during high load conditions.

Conclusions:

  • Genetic algorithms offer a potential method for improving network routing and QoS.
  • The effectiveness of GA-enhanced routing is dependent on network traffic load.
  • Further research is needed to optimize GA performance in high-traffic network environments.