Genetic Programming in Wireless Sensor Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{eurogp:JohnsonTS05,
  author =       "Derek M. Johnson and Ankur Teredesai and 
                 Robert T. Saltarelli",
  editor =       "Maarten Keijzer and Andrea Tettamanzi and 
                 Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
  title =        "Genetic Programming in Wireless Sensor Networks",
  booktitle =    "Proceedings of the 8th European Conference on Genetic
                 Programming",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3447",
  year =         "2005",
  address =      "Lausanne, Switzerland",
  month =        "30 " # mar # " - 1 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-25436-6",
  pages =        "96--107",
  URL =          "http://www.cs.rit.edu/~amt/pubs/EuroGP05FinalTeredesai.pdf",
  DOI =          "doi:10.1007/b107383",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "Wireless sensor networks (WSNs) are becoming
                 increasingly important as they attain greater
                 deployment. New techniques for evolutionary computing
                 (EC) are needed to address these new computing models.
                 This paper describes a novel effort to develop a series
                 of variations to evolutionary computing paradigms such
                 as Genetic Programming to enable their operation within
                 the wireless sensor network. The ability to compute
                 evolutionary algorithms within the WSN has innumerable
                 advantages including, intelligent-sensing, resource
                 optimised communication strategies, intelligent-routing
                 protocol design, novelty detection, etc to name a few.
                 In this paper we first discuss an evolutionary
                 computing algorithm that operates within a distributed
                 wireless sensor network. Such algorithms include
                 continuous evolutionary computing. Continuous
                 evolutionary computing extends the concept of an
                 asynchronous evolutionary cycle where each individual
                 resides and communicates with its immediate neighbours
                 in an asynchronous time-step and exchanges genetic
                 material. We then describe the adaptations required to
                 develop practicable implementations of evolutionary
                 computing algorithms to effectively work in resource
                 constrained environments such as WSNs. Several
                 adaptations including a novel representation scheme, an
                 approximate fitness computation method and a sufficient
                 statistics based data reduction technique lead to the
                 development of a GP implementation that is usable on
                 the low-power, small footprint architectures typical to
                 wireless sensor modes. We demonstrate the utility of
                 our formulations and validate the proposed ideas using
                 a variety of problem sets and describe the results.",
  notes =        "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
                 conjunction with EvoCOP2005 and EvoWorkshops2005",
}

Genetic Programming entries for Derek Michael Johnson Ankur M Teredesai Robert T Saltarelli

Citations