Genetic Programming in Wireless Sensor Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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:10.1007/978-3-540-31989-4_9",
• 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