Evolving distributed algorithms with genetic programming: election

  • Authors:
  • Thomas Weise;Michael Zapf

  • Affiliations:
  • University of Kassel, Kassel, Germany;University of Kassel, Kassel, Germany

  • Venue:
  • Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
  • Year:
  • 2009

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Abstract

In this paper, we present a detailed analysis of the application of Genetic Programming to the evolution of distributed algorithms. This research field has many facets which make it especially difficult. These aspects are discussed and countermeasures are provided. Six different Genetic Programming approaches (SGP, eSGP, LGP, RBGP, eRBGP, and Fraglets) are applied to the election problem as case study utilizing these countermeasures. The results of the experiments are analyzed statistically and discussed thoroughly.