An Evolutionary Approach to Network Self-Organization and Resilient Data Diffusion

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Andres J. Ramirez and Betty H. C. Cheng and 
                 Philip K. Mckinley",
  title =        "An Evolutionary Approach to Network Self-Organization
                 and Resilient Data Diffusion",
  booktitle =    "Fifth IEEE International Conference on Self-Adaptive
                 and Self-Organizing Systems, SASO 2011",
  year =         "2011",
  pages =        "198--207",
  address =      "Ann Arbor, MI, USA",
  month =        "3-7 " # oct,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 algorithm, cellular automata, self-organization, data
  ISSN =         "1949-3673",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1109/SASO.2011.31",
  size =         "10 pages",
  abstract =     "Data diffusion techniques enable a distributed system
                 to replicate and propagate data across a potentially
                 unreliable network in order to provide better data
                 protection and availability. This paper presents a
                 novel evolutionary computation approach to developing
                 network construction algorithms and data diffusion
                 strategies. The proposed approach combines a linear
                 genetic program with a cellular automaton to evolve
                 digital organisms (agents) capable of self-organising
                 into different types of networks and self-adapting to
                 changes in their surrounding environment, such as link
                 failures and node churn. We assess the effectiveness of
                 the proposed approach by conducting several experiments
                 that explore different network structures under
                 different environmental conditions. The results suggest
                 the combined methods are able to produce
                 self-organising and self-adaptive agents that construct
                 networks and efficiently distribute data throughout the
                 network, while balancing competing concerns, such as
                 minimising energy consumption and providing
  notes =        "Also known as \cite{6063502}",

Genetic Programming entries for Andres J Ramirez Betty H C Cheng Philip K McKinley